Nonlinear System Identification using Evolutionary Computing based Training Schemes
نویسنده
چکیده
The present work deals with application of recently developed evolutionary computing based training methods for non-linear system identification problem. Generally, most of the systems are nonlinear in nature. The conventionally used standard derivative based identification scheme does not work satisfactorily for nonlinear systems, which is due to premature settling of the model parameters. To prevent the premature settling of the weights, evolutionary computing based update algorithms have been proposed. In this paper we have compared three popular derivative free evolutionary computing based update algorithms namely Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) for identification of nonlinear systems, in terms of convergence graph of cost function over number of iterations. It has been demonstrated that the derivative free population based schemes provided excellent performance for identification of nonlinear systems and they are not trapped in problem of local minima as well.
منابع مشابه
Verification of an Evolutionary-based Wavelet Neural Network Model for Nonlinear Function Approximation
Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However, the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definiti...
متن کاملDynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks
Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...
متن کاملIdentification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network
Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different pur...
متن کاملAdaptive Network based Fuzzy Inference System (ANFIS) as a Tool for System Identification with Special Emphasis on Training Data Minimization
Nearly two decades back nonlinear system identification consisted of several ad-hoc approaches which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks, the fuzzy logic and the genetic algorithm combined with modern structure optimization techniques, a wider class of systems can be handled at present. Com...
متن کاملNonlinear System Identification using Opposition Based Learning Differential Evolution and Neural Network Techniques
The slow convergence and local minima problems associated with neural networks (NN) used for non-linear system identification have been resolved by evolutionary techniques such as differential evolution (DE) combined with Levenberg Marquardt (LM) algorithm. In this work the authors attempted further to employ an opposition based learning in DE, known as opposition based differential evolution (...
متن کامل