Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
The paper present a new learning algorithm for fuzzy neural network (FNN) systems to approximate unknown nonlinear continuous functions. The concept of exponential fast terminal sliding mode is introduced into the learning algorithm to improve approximation ability. The training algorithm guarantees that the approximation is stable and converges to the optimal approximation function with improved...
A new learning algorithm for fuzzy neural network (FNN) systems to approximate unknown nonlinear continuous functions is proposed. The concept of exponential fast terminal sliding mode is introduced into the learning algorithm to improve approximation ability. The Lyapunov stability analysis guarantees that the approximation is stable and converges to the unknown function with improved speed. The...
This paper presents a Simulink implementation of the CMAC controller which was proposed by J.S. Albus. The controller was implemented using the MatlAB S-function so that it can be used by a control engineer working on the Simulink platform to study the feasibility of using the CMAC controller for his control application. Simulations with various quantization levels and learning rates were done for...
In order to avoid the over fitting and training and solve the knowledge extraction problem in fuzzy neural networks system. A Lazy Learning Dynamic Fuzzy Neural Network (LL-DFNN) algorithm is proposed. The Learning Set based on Lazy Learning is constituted from input and output. Then the framework of Lazy Leaning Dynamic Fuzzy Neural Network is designed and its stability is proved. Finally, Simulation...
Use of unmanned Aerial Vehicles (UAVs) has gained significant importance in the recent years because of their ability to remotely monitor and perform various tasks in an autonomous manner. However, the control unit of such UAVs fails to adapt quickly when the UAVs are exposed to unpredictable and violent external disturbances such as violent wind gusts and extreme weather conditions. The cost of such...
This paper presents the design, implementation, and comparative analysis of two intelligent neural network based controllers employed for nonlinear dynamic compensation and adaptive trajectory tracking of a mobile robot system. The first control law is an integration of a backstepping controller with a neural network which is designed to learn the inverse dynamic model of the robot and to compensate...
This paper proposes a recurrent neural fuzzy network with the reinforcement improved particle swarm optimization (R-IPSO) for solving various control problems. The R-IPSO, which consists of structure learning and parameter learning, is also proposed. The structure learning is adopts several sub-swarms to constitute variable fuzzy systems and uses an elite-based structure strategy (ESS) to find suitable...
As an online learning algorithm of approximate dynamic programming (ADP), direct heuristic dynamic programming (DHDP) has demonstrated its applicability to large state and control problems. However, there still lacks of a systemic approach to initialize the network weights for DHDP. In this paper, an improved PID-neural network (IPIDNN) configuration is proposed and applied to the critic and action...
In this paper, a novel adaptive NN control scheme is proposed for a class of uncertain single-input and single- output(SISO) nonlinear time-delay systems with the lower triangular form. RBF NNs are used to approximate unknown nonlinear functions, then the adaptive NN tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the dynamic surface control(DSC) technique along...
This paper presents a method of continuous-time simple adaptive control (SAC) using neural network based on genetic algorithm (GA) for a single-input single-output (SISO) nonlinear systems, bounded-input bounded-output, and bounded nonlinearities. According to the power of neural network and the characteristics of simple adaptive control, constructed a simple adaptive control using neural networks,...
In order to solve the problem of dimension disaster, which may be produced by applying Q-learning to intelligent system of continuous state-space, we proposed a Q-learning algorithm based on ART2 in this paper, and give the specific steps. Through introducing the ART2 neural network in the Q-learning algorithm, Q-learning Agent in view of the duty which needs to complete to learn an appropriate incremental...
Iterative learning identification algorithms for time-varying neural networks training are presented, by which neural networks based identification for discrete-time varying nonlinear systems can be carried out, as the system undertaken performs tasks repeatedly over a finite time interval. This paper develops the iterative learning least squares algorithm with dead-zone for the weights updating along...
This paper presents the design of an iterative learning controller for a class of uncertain time-varying nonlinear systems in the presence of initial state errors. Through the introduction of initial rectified attractors and a finite-time dead-zone, a neural network iterative learning controller is designed with the proposed learning mechanism for the time-varying neural network training. The complete...
The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive...
A method of intelligent PID control was proved and it's based on RBF neural network and fuzzy theory, which constructs RBF neural network identifier online and identifies a controlled object online by means of adopting the receding horizon optimization methods, and adjusts parameters of PID controller online and realizes decoupling control of multivariable, nonlinear and time variation system. The...
In this paper, a novel neutral-network-based model is proposed to describe an X-Y macro-positioning stage. As the friction exists in the stage, the stage shows some complex behavior due to the non-smooth characteristic of the friction. In order to describe the non-smooth behavior of the stage, in this model, a non-smooth active function is proposed to construct the hidden neurons. Then, a training...
In this paper, a single-input cerebellar model articulation controller (CMAC)-based maximum power point tracking (MPPT) for PV system is proposed. As a type of neural network based controller with simple computation that results in fast learning, it is more suitable for hardware implementation. The single-input CMAC control system adopts two learning stages. During off-line learning stage the CMAC...
Based on the Object Oriented Flexible Design and Flexible Control theory, the concept of soft sensor object is pointed out in this paper, its structure, function and implementation are discussed. Soft sensor object is a new way for intelligent sensor design and a new method for sensor validation and sensor failure detection. A new training method for Auto-Associative Neural Network (AANN) is also...
A control method of neural network controller with reinforcement learning is proposed to implement idle speed control of an automobile engine to reduce fluctuation of the idle speed. Firstly, the reinforcement-learning neural network is demonstrated in detail. Then, the control scheme of the reinforcement-learning controller is designed to experiment. Q learning algorithm, as one of methods of reinforcement...
In this paper, design of a low cost autonomous vehicle based on neural network for navigation in unknown environments is presented. The vehicle is equipped with four ultrasonic sensors for hurdle distance measurement, a wheel encoder for measuring distance traveled, a compass for heading information, a GPS receiver for goal position information, a GSM modem for changing destination place on run time...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.