Aiming at the characteristics which variable air volume air conditioning system is multi-variable, nonlinear and uncertain system, normal fuzzy neural network is hard to meet the requirements which dynamic control of multi-variable. In this paper, we put forward a recursive neural network predictive control strategy based on wavelet neural network model. Through recursive wavelet neural network...

this paper proposes a hybrid control scheme for the synchronization of two chaotic duffing oscillator system, subject to uncertainties and external disturbances. the novelty of this scheme is that the linear quadratic regulation (lqr) control, sliding mode (sm) control and gaussian radial basis function neural network (grbfnn) control are combined to chaos synchronization with respect to extern...

this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

This paper proposes a novel motion controller for autonomous underwater vehicle based on parallel neural network. The motion controller consists of a real-time part, a self-learning part and a desired state programming part, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed with...

This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for selfadaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of conver...

Adaptive control of nonlinear systems has been an active area in recent years, but it is difficult to control unknown plants. A common approach to deal with this problem is to utilize the simultaneous identification technique. Neural networks have been employed in the identification and control of unknown nonlinear systems owing to their massive parallelism, fast adaptation and learning capabil...

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed contro...

In this paper, a neural network based adaptive sliding mode control scheme for hysteretic systems is proposed. In this control scheme, a neural network model is utilized to describe the characteristic of hysteresis. Then, the adaptive neural sliding mode controller based on the proposed neural model is presented for a class of single-input nonlinear systems with unknown hysteresis. For the case...

A nonlinear mathematical model of a feed-batch fermentation process of Bacillus thuringiensis (Bt.), is derived. The obtained model is validated by experimental data. Identification and direct adaptive neural control systems with and without integral term are proposed. The system contains a neural identifier and a neural controller, based on the recurrent trainable neural network model. The app...