A New Version of Elman Neural Networks for Dynamic Systems Modeling and Control
نویسنده
چکیده
Elman network is a class of recurrent neural networks used for function approximation. The main problem of this class is that its structure has a set of global sigmoid functions at its hidden layer. That means that if the operating conditions of a process be identified, are changed the function approximation property of the network is degraded. This paper introduces a new version of the Elman network named Elman Recurrent Wavelet Neural Network (ERWNN). It merges the multiresolution property of the wavelets and the learning capabilities of the Elman neural network to inherit the advantages of the two paradigms and to avoid their drawbacks. Stability and convergence property is proven for the proposed network. The paper also develops a model reference control scheme using the proposed ERWNN. The proposed scheme belongs to indirect adaptive control schemes. The dynamic back propagation (DBP) algorithm is employed to train both the two networks structured for the indirect control scheme. This paper derives also the plant sensitivity for adjusting the parameters of the developed controller. The advantages of this new version of ERWNN in modeling and controlling time intensive dynamic processes, are reflected in our simulation results.
منابع مشابه
A New Version of Elman Neural Networks for Dynamic Systems Modeling
Elman network is a class of recurrent neural networks used for function approximation. It has a set of global sigmoid functions at its hidden units. That means that if the operating conditions of a process be identified, are changed the function approximation property of the network is degraded. This is due to the fact that the universes of discourse of the network is covered by global sigmoid ...
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 کاملModeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)
متن کامل
Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks
Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...
متن کاملNeuro-ACT Cognitive Architecture Applications in Modeling Driver’s Steering Behavior in Turns
Cognitive Architectures (CAs) are the core of artificial cognitive systems. A CA is supposed to specify the human brain at a level of abstraction suitable for explaining how it achieves the functions of the mind. Over the years a number of distinct CAs have been proposed by different authors and their limitations and potentials were investigated. These CAs are usually classified as symbolic and...
متن کامل