Evolving MIMO Flexible Neural Trees for Nonlinear System Identification
نویسندگان
چکیده
The problem of identification of a nonlinear dynamic system by using multiple-input and multiple-output flexible neural tree (MIMO-FNT) is presented in this paper. This work is an extension of our previously multiple-input and singleoutput FNT model. FNT is a tree-structured neural networks which allows input variables selection, over-layer connections and different activation functions for different nodes. Based on the pre-defined instruction set, a FNT model can be created and evolved. In this research, the MIMO FNT structure is developed using the Immune Programming (IP) and the free parameters embedded in the neural tree are optimized by Particle Swarm Optimization (PSO) algorithm. Empirical results on nonlinear dynamical system identification problems indicate that the proposed method is effective and efficient.
منابع مشابه
Identification 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 Neural Network Method for Consensus Tracking of High-Order Mimo Nonlinear Multi-Agent Systems
This paper is concerned with the consensus tracking problem of high order MIMO nonlinear multi-agent systems. The agents must follow a leader node in presence of unknown dynamics and uncertain external disturbances. The communication network topology of agents is assumed to be a fixed undirected graph. A distributed adaptive control method is proposed to solve the consensus problem utilizing re...
متن کاملPotentials of Evolving Linear Models in Tracking Control Design for Nonlinear Variable Structure Systems
Evolving models have found applications in many real world systems. In this paper, potentials of the Evolving Linear Models (ELMs) in tracking control design for nonlinear variable structure systems are introduced. At first, an ELM is introduced as a dynamic single input, single output (SISO) linear model whose parameters as well as dynamic orders of input and output signals can change through ...
متن کاملAdaptive Leader-Following and Leaderless Consensus of a Class of Nonlinear Systems Using Neural Networks
This paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (MIMO) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. The agents may have different dynamics and communicate together under a directed graph. A distributed adaptive method is designed for both cases. The structures of the contro...
متن کاملNonlinear modeling of MCFC stack based on RBF neural networks identification
Modelling Molten Carbonate Fuel Cells (MCFC) is very difficult and the existing models are too complicated to be used for controlling design, especially for on-line control design. This paper presents the application of neural networks identification method to develop the nonlinear temperature model of MCFC stack. The hidden layer units of the neural networks consist of a set of nonlinear radia...
متن کامل