Functional Approximation Using Neuro-genetic Hybrid Systems
نویسنده
چکیده
Artificial neural networks provide a methodology for solving many types of nonlinear problems that are difficult to solve using traditional techniques. Neurogenetic hybrid systems bring together the artificial neural networks benefits and the inherent advantages of evolutionary algorithms. A functional approximation method using neuro-genetic hybrid systems is proposed in this paper. Three evolutionary algorithms are used: simple evolutionary algorithm, adaptive evolutionary algorithm and differential evolution. It is also proposed an optimization method for convergence lapse of evolutionary algorithms using a hybrid technique for training neural networks, combining an algorithm based on the gradient descent (backpropagation) and evolutionary algorithms.
منابع مشابه
Adaptive Online Traffic Flow Prediction Using Aggregated Neuro Fuzzy Approach
Short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. Although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . These results made the hybrid tools and approaches a more common method for ...
متن کاملA Genetic-Based Neuro-Fuzzy Generator: NEFGEN
This paper is concemed with the design of new generation of intelligent systems. These systems or machines are intelligent if they are able to improve their performance or maintain an acceptable level of performance in the presence of uncertainty. The ability of these systems to examine and modifr their behaviors in a limited sense is usually achieved by using techniques such as knowledge-based...
متن کاملDesigninga Neuro-Sliding Mode Controller for Networked Control Systems with Packet Dropout
This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control system...
متن کاملReliability and Sensitivity Analysis of Structures Using Adaptive Neuro-Fuzzy Systems
In this study, an efficient method based on Monte Carlo simulation, utilized with Adaptive Neuro-Fuzzy Inference System (ANFIS) is introduced for reliability analysis of structures. Monte Carlo Simulation is capable of solving a broad range of reliability problems. However, the amount of computational efforts that may involve is a draw back of such methods. ANFIS is capable of approximating str...
متن کاملTime Series Model Mining with Similarity-Based Neuro-Fuzzy Networks and Genetic Algorithms: A Parallel Implementation
This paper presents a parallel implementation of a hybrid data mining technique for multivariate heterogeneous time varying processes based on a combination of neuro-fuzzy techniques and genetic algorithms. The purpose is to discover patterns of dependency in general multivariate time-varying systems, and to construct a suitable representation for the function expressing those dependencies. The...
متن کامل