In this paper, we consider robust control of nonlinear systems, via inclusion nonlinear systems solution and $H_{\infty}$ controller using singular perturbation method. First, using a technique for solving inclusion nonlinear systems, we transform the nonlinear system to an ordinary nonlinear system. Then using normal form equations, we eliminate the nonlinear part of the system matrix of equations of the system and transform it to a linear diagonal form. Separating new equations into slow and fast subsystems, due to the singular perturbation method and with the assumption of norm-boundedness of the fast dynamics, we can treat them as disturbance and design an $H_{\infty}$ controller for a system with a lower order than the original one that stabilizes the overall closed loop system. The proposed method is applied to a nominal system.
The main purpose of using the suspension system in vehicles is to prevent the road disturbance from being transmitted to the passengers. Therefore, a precise controller should be designed to improve the performances of suspension system. This paper presents a modeling and control of the nonlinear full vehicle active suspension system with passenger seat utilizing Fuzzy Model Reference Learning Control (FMRLC) technique. The components of the suspension system are: damper, spring and actuator, all of those components have nonlinear behavior, so that, nonlinear forces that are generated by those components should be taken into account when designed the control system. The designed controller consumes high power so that when the control system is used, the vehicle will consume high amount of fuel. It notes that, when vehicle is driven on a rough road; there will be a shock between the sprung mass and the unsprung mass. This mechanical power dissipates and converts into heat power by a damper. In this paper, the wasted power has reclaimed in a proper way by using electromagnetic actuator. The electromagnetic actuator converts the mechanical power into electrical power which can be used to drive the control system. Therefore, overall power consumption demand for the vehicle can be reduced. When the electromagnetic actuator is used three main advantages can be obtained: firstly, fuel consumption by the vehicle is decreased, secondly, the harmful emission is decreases, therefore, our environment is protected, and thirdly, the performance of the suspension system is improved as shown in the obtained results.
This review article puts forward the phenomena of chaotic oscillation in electrical power systems. The aim is to present some short summaries written by distinguished researchers in the field of chaotic oscillation in power systems. The reviewed papers are classified according to the phenomena that cause the chaotic oscillations in electrical power systems. Modern electrical power systems are evolving day by day from small networks toward large-scale grids. Electrical power systems are constituted of multiple inter-linked together elements, such as synchronous generators, transformers, transmission lines, linear and nonlinear loads, and many other devices. Most of these components are inherently nonlinear in nature rendering the whole electrical power system as a complex nonlinear network. Nonlinear systems can evolve very complex dynamics such as static and dynamic bifurcations and may also behave chaotically. Chaos in electrical power systems is very unwanted as it can drive system bus voltage to instability and can lead to voltage collapse and ultimately cause a general blackout.
In light of the widespread usage of power electronics devices, power quality (PQ) has become an increasingly essential factor. Due to nonlinear characteristics, the power electronic devices produce harmonics and consume lag current from the utility. The UPQC is a device that compensates for harmonics and reactive power while also reducing problems related to voltage and current. In this work, a three-phase, three-wire UPQC is suggested to reduce voltage-sag, voltage-swell, voltage and current harmonics. The UPQC is composed of shunt and series Active Power Filters (APFs) that are controlled utilizing the Unit Vector Template Generation (UVTG) technique. Under nonlinear loads, the suggested UPQC system can be improved PQ at the point of common coupling (PCC) in power distribution networks. The simulation results show that UPQC reduces the effect of supply voltage changes and harmonic currents on the power line under nonlinear loads, where the Total Harmonic Distortion (THD) of load voltages and source currents obtained are less than 5%, according to the IEEE-519 standard.
Hybrid electric vehicles have received considerable attention because of their ability to improve fuel consumption compared to conventional vehicles. In this paper, a series-parallel hybrid electric vehicle is used because they combine the advantages of the other two configurations. In this paper, the control unit for a series-parallel hybrid electric vehicle is implemented using a Nonlinear Model Predictive Control (NMPC) strategy. The NMPC strategy needs to create a vehicle energy management optimization problem, which consists of the cost function and its constraints. The cost function describes the required control objectives, which are to improve fuel consumption and obtain a good dynamic response to the required speed while maintaining a stable value of the state of charge (SOC) for batteries. While the cost function is subject to the physical constraints and the mathematical prediction model that evaluate vehicle's behavior based on the current vehicle measurements. The optimization problem is solved at each sampling step using the (SQP) algorithm to obtain the optimum operating points of the vehicle's energy converters, which are represented by the torque of the vehicle components.
The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PI λ D μ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.
Growing interests in nature-inspired computing and bio-inspired optimization techniques have led to powerful tools for solving learning problems and analyzing large datasets. Several methods have been utilized to create superior performance-based optimization algorithms. However, certain applications, like nonlinear real-time, are difficult to explain using accurate mathematical models. Such large-scale combination and highly nonlinear modeling problems are solved by usage of soft computing techniques. So, in this paper, the researchers have tried to incorporate one of the most advanced plant algorithms known as Venus Flytrap Plant algorithm(VFO) along with soft-computing techniques and, to be specific, the ANFIS inverse model-Adaptive Neural Fuzzy Inference System for controlling the real-time temperature of a microwave cavity that heats oil. The MATLAB was integrated successfully with the LabVIEW platform. Wide ranges of input and output variables were experimented with. Problems were encountered due to heating system conditions like reflected power, variations in oil temperature, and oil inlet absorption and cavity temperatures affecting the oil temperature, besides the temperature’s effect on viscosity. The LabVIEW design followed and the results figure in the performance of the VFO- Inverse ANFIS controller.
In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller’s efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC’s superior performance, underscoring its potential as a formidable solution in power system regulation.
Mathematical modeling is very effective method to investigate interaction between insulin and glucose. In this paper, a new mathematical model for insulin-glucose regulation system is introduced based on well-known Lokta-Volterra model. Chaos is a common property in complex biological systems in the previous studies. The results here are in accordance with previous ones and indicating that insulin-glucose regulating system has many dynamics in different situations. The overall result of this paper may be helpful for better understanding of diabetes mellitus regulation system including diseases such as hyperinsulinemia and Type1 DM.
Induction Motor (IM) speed control is an area of research that has been in prominence for some time now. In this paper, a nonlinear controller is presented for IM drives. The nonlinear controller is designed based on input-output feedback linearization control technique, combined with sliding mode control (SMC) to obtain a robust, fast and precise control of IM speed. The input-output feedback linearization control decouples the flux control from the speed control and makes the synthesis of linear controllers possible. To validate the performances of the proposed control scheme, we provided a series of simulation results and a comparative study between the performances of the proposed control strategy and those of the feedback linearization control (FLC) schemes. Simulation results show that the proposed control strategy scheme shows better performance than the FLC strategy in the face of system parameters variation.
The identification of system parameters plays an essential role in system modeling and control. This paper presents a parameter estimation for a permanent magnetic DC motor using the simulink design optimization method. The parameter estimation may be represented as an optimization problem. Firstly, the initial values of the DC motor parameters are extracted using the dynamic model through measuring the values of voltage, current, and speed of the motor. Then, these values are used as an initial value for simulink design optimization. The experimentally input- output data can be collected using a suggested microcontroller based circuit that will be used later for estimating the DC motor parameters by building a simulink model. Two optimization algorithms are used, the pattern search and the nonlinear least square. The results show that the nonlinear least square algorithm gives a more accurate result that almost approaches to the actual measured speed response of the motor. )
The objective of this paper is to design an efficient control scheme for car suspension system. The purpose of suspension system in vehicles is to get more comfortable riding and good handling with road vibrations. A nonlinear hydraulic actuator is connected to passive suspension system in parallel with damper. The Particles Swarm Optimization is used to tune a PID controller for active suspension system. The designed controller is applied for quarter car suspension system and result is compared with passive suspension system model and input road profile. Simulation results show good performance for the designed controller I. I NTRODUCTION Suspensions systems can be classified into three types are (passive, simi active and active). Figs. 1, 2 and 3 below shows the three types of Quarter car suspension system and hydraulic actuator position in each type.[1] Fig. 1 Passive Quarter Car Model Fig. 2 Simi-Active Quarter Car Model Fig. 3 Active Quarter Car Model In passive suspension systems the main parts are springs and hydraulic dumpers. The main job of these dumpers is to decrease the road profile and vibration effects into driver and passenger’s cabin. In active suspension system there are three parts under spring mass (body of car), spring, dumper and hydraulic actuator are connected in parallel. In this paper an additional parts is added to passive suspension system in parallel with springs and dumpers called a hydraulic actuator to get an active suspension system. This hydraulic actuator is a nonlinear part and it is controlled by spool valve. The mechanism of this actuator is to decrease the road profile and vibration from passive suspension system to get more comfortable riding. By using PID controller trained by Particle Swarm Optimization (PSO) to find optimal values of proportional, divertive and Quarter Car Active Suspension System Control Using PID Controller tuned by PSO Wissam H. Al-Mutar Turki Y. Abdalla Electrical Eng. Computer Eng. University of Basrah University of Basrah Basrah. Iraq. Basrah. Iraq. Spring Mass Unpring Mass K Kt C Ct Spring Mass Unpring K K C C Spring Mass Unpring Mass K Kt C F Ct اﻟﻤﺠﻠﺔ اﻟﻌﺮاﻗﻴﺔ ﻟﻠﻬﻨﺪﺳﺔ اﻟﻜﻬﺮﺑﺎﺋﻴﺔ واﻻﻟﻜﺘﺮوﻧﻴﺔ Iraq J. Electrical and Electronic Engineering ﻡﺠﻠﺪ 11 ، اﻟﻌﺪد 2 ، 2015 Vol.11 No.2 , 2015 Active suspension, PSO, PID controller, quarter car
The control problem for a class of a nonlinear systems that contain the coupling of unmeasured states and unknown parameters is addressed. The system actuation is assumed to suffer from unknown dead zone nonlinearity. The parameters bounds of the unknown dead zone to be considered are unknown. Adaptive sliding mode controller, unmeasured states observer, and unknown parameters estimators are suggested such that global stability is achieved. Simulation for a single link mechanical system with unknown dead zone and friction torque is implemented for proving the efficacy of the suggested scheme.
In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.
The gyroscope and accelerometer are the basic sensors used by most Unmanned Aerial Vehicle (UAV) like quadcopter to control itself. In this paper, the fault detection of measured angular and linear states by gyroscope and accelerometer sensors are present. Uncertainties in measurement and physical sensors itself are the main reasons that lead to generate noise and cause the fault in measured states. Most previous solutions are process angular or linear states to improving the performance of quadcopter. Also, in most of the previous solutions, KF and EKF filters are used, which are inefficient in dealing with high nonlinearity systems such as quadcopter. The proposed algorithm is developed by the robust nonlinear filter, Unscented Kalman Filter (UKF), as an angular and linear estimation filter. Simulation results show that the proposed algorithm is efficient to decrease the effect of sensors noise and estimate accurate angular and linear states. Also, improving the stability and performance properties of the quadcopter. In addition, the new algorithm leads to increasing the range of nonlinearity movements that quadcopter can perform it.
This work focuses on the use of the Linear Quadratic Gaussian (LQG) technique to construct a reliable Static VAr Compensator (SVC), Thyristor Controlled Series Compensator (TCSC), and Excitation System controller for damping Subsynchronous Resonance ( SSR ) in a power system. There is only one quantifiable feedback signal used by the controller (generator speed deviation). It is also possible to purchase this controller in a reduced-order form. The findings of the robust control are contrasted with those of the "idealistic" full state optimal control. The LQG damping controller's regulator robustness is then strengthened by the application of Loop Transfer Recovery (LTR). Nonlinear power system simulation is used to confirm the resilience of the planned controller and demonstrates how well the regulator dampens power system oscillations. The approach dampens all torsional oscillatory modes quickly while maintaining appropriate control actions, according to simulation results.
In this paper we have proposed a non- linear mathematical model for a wind turbine. The objective function maximizes the power of the wind turbine and the constraints are related to the rotor and tower costs. Rotor diameter and hub height are the variables which affect on power of the wind turbine, so we have considered them as decision variable in our mathematical model. By increasing rotor diameter and hub height the power of the turbine will increase but the costs don’t let the infinitive increase in rotor diameter and height. The model applied for a typical case study and the results of solving the model for it have shown in the paper.
In this study, the chaotic dynamics observed from a vertical cavity surface emitting lasers (VCSELS) subject to delayed optoelectronic feedback are investigated. The theoretical investigation is performed by using a MATLAB software package. The nonlinear dynamics of a VCSEL are examined using a single mode rate equations model. The key role played by system parameters such as delay time and the feedback strength on laser chaotic dynamics is addressed.
Recently, chaos theory has been widely used in multimedia and digital communications due to its unique properties that can enhance security, data compression, and signal processing. It plays a significant role in securing digital images and protecting sensitive visual information from unauthorized access, tampering, and interception. In this regard, chaotic signals are used in image encryption to empower the security; that’s because chaotic systems are characterized by their sensitivity to initial conditions, and their unpredictable and seemingly random behavior. In particular, hyper-chaotic systems involve multiple chaotic systems interacting with each other. These systems can introduce more randomness and complexity, leading to stronger encryption techniques. In this paper, Hyper-chaotic Lorenz system is considered to design robust image encryption/ decryption system based on master-slave synchronization. Firstly, the rich dynamic characteristics of this system is studied using analytical and numerical nonlinear analysis tools. Next, the image secure system has been implemented through Field-Programmable Gate Arrays (FPGAs) Zedboard Zynq xc7z020-1clg484 to verify the image encryption/decryption directly on programmable hardware Kit. Numerical simulations, hardware implementation, and cryptanalysis tools are conducted to validate the effectiveness and robustness of the proposed system.
In this paper, we present a new programmable chaotic circuit based on the dynamical chaotic system introduced by E. Lorenz. The design and realization of the model are accomplished by using a programmable logic controller (PLC). The system can be modeled and realized with a structured texted. The nonlinear differential equations of Lorenz model are solved numerically. The generated chaotic signal by using PLC is applied to a single- phase induction motor via a variable frequency drive to create a chaotic perturbation in the experiments of liquid mixing. Colorization liquid experiments shows that the generated chaotic motion effectively makes an enhancement of the mixing process in the stirred-tank mixer model in our laboratory.
Various methods have been exploited in the blind source separation problems, especially in cocktail party problems. The most commonly used method is the independent component analysis (ICA). Many linear and nonlinear ICA methods, such as the radial basis functions (RBF) and self-organizing map (SOM) methods utilise neural networks and genetic algorithms as optimisation methods. For the contrast function, most of the traditional methods, especially the neural networks, use the gradient descent as an objective function for the ICA method. Most of these methods trap in local minima and consume numerous computation requirements. Three metaheuristic optimisation methods, namely particle, quantum particle, and glowworm swarm optimisation methods are introduced in this study to enhance the existing ICA methods. The proposed methods exhibit better results in separation than those in the traditional methods according to the following separation quality measurements: signal-to-noise ratio, signal-to-interference ratio, log-likelihood ratio, perceptual evaluation speech quality and computation time. These methods effectively achieved an independent identical distribution condition when the sampling frequency of the signals is 8 kHz.
This paper deals with the application of Fuzzy-Neural Networks (FNNs) in multi-machine system control applied on hot steel rolling. The electrical drives that used in rolling system are a set of three-phase induction motors (IM) controlled by indirect field-oriented control (IFO). The fundamental goal of this type of control is to eliminate the coupling influence though the coordinate transformation in order to make the AC motor behaves like a separately excited DC motor. Then use Fuzzy-Neural Network in control the IM speed and the rolling plant. In this work MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results are presented to verify the effectiveness of the proposed control schemes. It is found that the proposed system is robust in that it eliminates the disturbances considerably.
In this paper, a new nonlinear dynamic system, new three-dimensional fractional order complex chaotic system, is presented. This new system can display hidden chaotic attractors or self-excited chaotic attractors. The Dynamic behaviors of this system have been considered analytically and numerically. Different means including the equilibria, chaotic attractor phase portraits, the Lyapunov exponent, and the bifurcation diagrams are investigated to show the chaos behavior in this new system. Also, a synchronization technique between two identical new systems has been developed in master- slave configuration. The two identical systems are synchronized quickly. Furthermore, the master-slave synchronization is applied in secure communication scheme based on chaotic masking technique. In the application, it is noted that the message is encrypted and transmitted with high security in the transmitter side, in the other hand the original message has been discovered with high accuracy in the receiver side. The corresponding numerical simulation results proved the efficacy and practicability of the developed synchronization technique and its application
In this article, a robust control technique for 2-DOF helicopter system is presented. The 2-DOF helicopter system is 2 inputs and 2 outputs system that is suffering from the high nonlinearity and strong coupling. This paper focuses on design a simple, robust, and optimal controller for the helicopter system. Moreover, The proposed control method takes into account effects of the measurement noise in the closed loop system that effect on the performance of controller as well as the external disturbance. The proposed controller combines low pass filter with robust PID controller to ensure good tracking performance with high robustness. A low pass filter and PID controller are designed based H∞weighted mixed sensitivity. Nonlinear dynamic model of 2-DOF helicopter system linearized and then decoupled into pitch and yaw models. Finally, proposed controller applied for each model. Matlab program is used to check effectiveness the proposed control method. Simulation results show that the proposed controllers has best tracking performance with no overshot and the smallest settling time with respect to standard H∞and optimized PID controller.
In this paper, a model of PM DC Motor Drive is presented. The nonlinear dynamics of PM DC Motor Drive is discussed. The drive system shows different dynamical behaviors; periodic, quasi-period, and chaotic and are characterized by bifurcation diagrams, time series evolution, and phase portrait. The stabilization of chaos to a fixed point is adopted using slide mode controller (SMC). The chaotic dynamics are suppressed and the fixed point dynamics are observed after the activation of proposed controller. Numerical simulation results show the effectiveness of the proposed method of control for stabilization the chaos and different disturbances in the system.
In this work, a new flux controlled memristor circuit is presented. It provides a tool to emulate the pinched hysteresis loop. When driven the memristor by a bipolar periodic signal, the memristor exhibits a “pinched hysteresis loop” in the voltage-current plane and starting from some critical frequency, the hysteresis lobe area decreases monotonically as the excitation frequency increases, the pinched hysteresis loop shrinks to a single-valued function when the frequency tends to infinity. The design model numerically simulated and the physical implementation is achieved by using a field programmable analog array (FPAA). The circuit can be modeled and implemented with a changeable nonlinear function blocks and fixed main system blocks. The simplicity of the specific design method makes this proposed model be a very engaging option for the design of the memristor .
In this article, a PD-type iterative learning control algorithm (ILC) is proposed to a nonlinear time-varying system for cases of measurement disturbances and the initial state errors. The proposed control approach uses a simple structure and has an easy implementation. The iterative learning controller was utilized to control a constant current source inverter (CSI) with pulse width modulation (PWM); subsequently the output current trajectory converged the sinusoidal reference signal and provided constant switching frequency. The learning controller's parameters were tuned using particle swarm optimization approach to get best optimal control for the system output. The tracking error limit is achieved using the convergence exploration. The proposed learning control scheme was robust against the error in initial conditions and disturbances which outcome from the system modeling inaccuracies and uncertainties. It could correct the distortion of the inverter output current waveform with less computation and less complexity. The proposed algorithm was proved mathematically and through computer simulation. The proposed optimal learning method demonstrated good performances.
Control of Induction Motor (IM) is well known to be difficult owing to the fact the models of IM are highly nonlinear and time variant. In this paper, to achieve accurate control performance of rotor position control of IM, a new method is proposed by using adaptive inverse control (AIC) technique. In recent years, AIC is a very vivid field because of its advantages. It is quite different from the traditional control. AIC is actually an open loop control scheme and so in the AIC the instability problem cased by feedback control is avoided and the better dynamic performances can also be achieved. The model of IM is identified using adaptive filter as well as the inverse model of the IM, which was used as a controller. The significant of using the inverse of the IM dynamic as a controller is to makes the IM output response to converge to the reference input signal. To validate the performances of the proposed new control scheme, we provided a series of simulation results.
This paper presents a simplified control method for three-phase active power filter by calculating the required reactive and harmonics current of the load. The active power filter needs this current to correct the power factor and eliminate the generated harmonics by nonlinear loads. This method has the advantages of using only one current sensor and effectiveness in achieving the required compensation characteristics. The proposed circuit may be operate at frequencies ranging from 40 to 60 Hz, and it also responds very fast under sudden changes in the load conditions. The considered system is analyzed and a prototype is also developed and tested to demonstrate the performance of the implemented active power filter in the power factor improvement and harmonics elimination. Finally, predicted results are verified experimentally.
In this paper, three phase induction motor (IM) has been modelled in stationary reference frame and controlled by using direct torque control (DTC) method with constant V/F ratio. The obtained drive system consists of nine nonlinear first order differential equations. The numerical analysis is used to investigate the system behavior due to control parameter change. The integral gain of speed loop is used as bifurcation parameter to test the system dynamics. The simulation results show that the system has period-doubling route to chaos, period-1, period-2, period-4, and then the system gets chaotic oscillation. A specific value of the parameter range shows that the system has very strong randomness and a high degree of disturbance
In the present-day decade, the world has regarded an expansion in the use of non-linear loads. These a lot draw harmonic non- sinusoidal currents and voltages in the connection factor with the utility and distribute them with the useful resource of the overall performance of it. The propagation of these currents and voltages into the grids have an effect on the electricity constructions in addition to the one of various client equipment. As a result, the electrical strength notable has come to be critical trouble for each client and distributor of electrical power. Active electrical electricity filters have been proposed as environment splendid gear for electrical power pinnacle notch enchantment and reactive electrical strength compensation. Active Power Filters (APFs) have Flipped out to be a possible wish in mitigating the harmonics and reactive electrical electricity compensation in single-phase and three-phase AC electrical energy networks with Non-Linear Loads (NLLs). Conventionally, this paper applied Ant Colony Algorithm (ACO) for tuning PI and reduce Total Harmonic Distortion (THD). The result show reduces THD at 2.33%.
The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been proposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN and Neuro-fuzzy structured trained. The result obtained had been compared with conventional Box-Jenkins models (ARIMA models) as a statistical method used in time series analysis. ARIMA (Autoregressive integrated moving average) is one of the statistical models that utilized in time series prediction during the last several decades. Neuro- Fuzzy Modeling was used to build the prediction system, which give effective in improving the predict operation efficiency. To train the prediction system, a historical data were used. The data representing the monthly electric consumption in Basra city during the period from (Jan 2005 to Dec 2011). The data utilized to compare the proposed model and the forecasting of demand for the subsequent two years (Jan 2012-Dec 2013). The results give the efficiency of proposed methodology and show the good performance of the proposed Neuro-fuzzy method compared with the traditional ARIMA method.
Audio encryption has gained popularity in a variety of fields including education, banking over the phone, military, and private audio conferences. Data encryption algorithms are necessary for processing and sending sensitive information in the context of secure speech conversations. In recent years, the importance of security in any communications system has increased. To transfer data securely, a variety of methods have been used. Chaotic system-based encryption is one of the most significant encryption methods used in the field of security. Chaos-based communication is a promising application of chaos theory and nonlinear dynamics. In this research, a chaotic algorithm for the new chaotic chameleon system was proposed, studied, and implemented. The chameleon chaotic system has been preferred to be employed because it has the property of changing from self-excited (SA) to hidden-attractor (HA) which increases the complexity of the system dynamics and gives strength to the encryption algorithm. A chaotic chameleon system is one in which, depending on the parameter values, the chaotic attractor alternates between being a hidden attractor and a self-excited attractor. This is an important feature, so it is preferable to use it in cryptography compared to other types of chaotic systems. This model was first implemented using a Field Programmable Gate Array (FPGA), which is the first time it has been implemented in practical applications. The chameleon system model was implemented using MATLAB Simulink and the Xilinx System Generator model. Self-excited, hidden, and coexisting attractors are shown in the proposed system. Vivado software was used to validate the designs, and Xilinx ZedBoard Zynq-7000 FPGA was used to implement them. The dynamic behavior of the proposed chaotic system was also studied and analysis methods, including phase portrait, bifurcation diagrams, and Lyapunov exponents. Assessing the quality of the suggested method by doing analyses of many quality measures, including correlation, differential signal-to-noise ratio (SNR), entropy, histogram analysis, and spectral density plot. The numerical analyses and simulation results demonstrate how well the suggested method performs in terms of security against different types of cryptographic assaults.