Hong-Gui Han

An efficient self-organizing RBF neural network for water quality prediction

This paper presents a flexible structure Radial Basis Function (RBF) neural network (FS-RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure dynamically in order to maintain the prediction accuracy. The hidden neurons in the RBF neural network can be added or removed online based on the neuron activity and mutual information (MI), to achieve the appropriate network complexity and maintain overall computational efficiency. The convergence of the algorithm is analyzed in both the dynamic process phase and the phase following the modification of the structure. The proposed FS-RBFNN has been tested and compared to other algorithms by applying it to the problem of identifying a nonlinear dynamic system. Experimental results show that the FS-RBFNN can be used to design an RBF structure which has fewer hidden neurons; the training time is also much faster. The algorithm is applied for predicting water quality in the wastewater treatment process. The results demonstrate its effectiveness.

Adaptive Computation Algorithm for RBF Neural Network

A novel learning algorithm is proposed for nonlinear modelling and identification using radial basis function neural networks. The proposed method simplifies neural network training through the use of an adaptive computation algorithm (ACA). In addition, the convergence of the ACA is analyzed by the Lyapunov criterion. The proposed algorithm offers two important advantages. First, the model performance can be significantly improved through ACA, and the modelling error is uniformly ultimately bounded. Secondly, the proposed ACA can reduce computational cost and accelerate the training speed. The proposed method is then employed to model classical nonlinear system with limit cycle and to identify nonlinear dynamic system, exhibiting the effectiveness of the proposed algorithm. Computational complexity analysis and simulation results demonstrate its effectiveness.

Nonlinear Model-Predictive Control for Industrial Processes: An Application to Wastewater Treatment Process

Because of their complex behavior, wastewater treatment processes (WWTPs) are very difficult to control. In this paper, the design and implementation of a nonlinear model-predictive control (NMPC) system are discussed. The proposed NMPC comprises a self-organizing radial basis function neural network (SORBFNN) identifier and a multiobjective optimization method. The SORBFNN with concurrent structure and parameter learning is developed as a model identifier for approximating the online states of dynamic systems. Then, the solution of the multiobjective optimization is obtained by a gradient method which can shorten the solution time of optimal control problems. Moreover, the conditions for the stability analysis of NMPC are presented. Experiments reveal that the proposed control technique gives satisfactory tracking and disturbance rejection performance for WWTPs. Experimental results on a real WWTP show the efficacy of the proposed NMPC for industrial processes in many applications.

Nonlinear Systems Modeling Based on Self-Organizing Fuzzy-Neural-Network With Adaptive Computation Algorithm

In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.

A REPAIR ALGORITHM FOR RADIAL BASIS FUNCTION NEURAL NETWORK AND ITS APPLICATION TO CHEMICAL OXYGEN DEMAND MODELING

This paper presents a repair algorithm for the design of a Radial Basis Function (RBF) neural network. The proposed repair RBF (RRBF) algorithm starts from a single prototype randomly initialized in the feature space. The algorithm has two main phases: an architecture learning phase and a parameter adjustment phase. The architecture learning phase uses a repair strategy based on a sensitivity analysis (SA) of the networks output to judge when and where hidden nodes should be added to the network. New nodes are added to repair the architecture when the prototype does not meet the requirements. The parameter adjustment phase uses an adjustment strategy where the capabilities of the network are improved by modifying all the weights. The algorithm is applied to two application areas: approximating a non-linear function, and modeling the key parameter, chemical oxygen demand (COD) used in the waste water treatment process. The results of simulation show that the algorithm provides an efficient solution to both problems.

Adaptive dissolved oxygen control based on dynamic structure neural network

Activated sludge wastewater treatment processes (WWTPs) are difficult to control because of their complex nonlinear behavior. In this paper, an adaptive controller based on a dynamic structure neural network (ACDSNN) is proposed to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). The proposed ACDSNN incorporates a structure variable feedforward neural network (FNN), where the FNN can determine its structure on-line automatically. The structure of the FNN is adapted to cope with changes in the operating characteristics, while the weight parameters are updated to improve the accuracy of the controller. A particularly strong feature of this method is that the control accuracy can be maintained during adaptation, and therefore the control performance will not be degraded when the character of the model changes. The performance of the proposed ACDSNN is illustrated with numerical simulations and is compared with the fixed structure fuzzy and FNN approaches; it provides an effective solution to the control of the DO concentration in a WWTP.

A structure optimisation algorithm for feedforward neural network construction

This paper proposes a constructing-and-pruning (CP) approach to optimise the structure of a feedforward neural network (FNN) with a single hidden layer. The number of hidden nodes or neurons is determined by their contribution ratios, which are calculated using a Fourier decomposition of the variance of the FNNs output. Hidden nodes with sufficiently small contribution ratios will be eliminated, while new nodes will be added when the FNN cannot satisfy certain design objectives. This procedure is similar to the growing and pruning processes observed in biological neural networks. The performance of the proposed method is evaluated using a number of examples: real-life date classification, dynamic system identification, and the key variables modelling in a wastewater treatment system. Experimental results show that the proposed method effectively optimises the network structure and performs better than some existing algorithms.

Real-Time Model Predictive Control Using a Self-Organizing Neural Network

In this paper, a real-time model predictive control (RT-MPC) based on self-organizing radial basis function neural network (SORBFNN) is proposed for nonlinear systems. This RT-MPC has its simplicity in parallelism to model predictive control design and efficiency to deal with computational complexity. First, a SORBFNN with concurrent structure and parameter learning is developed as the predictive model of the nonlinear systems. The model performance can be significantly improved through SORBFNN, and the modeling error is uniformly ultimately bounded. Second, a fast gradient method (GM) is enhanced for the solution of optimal control problem. This proposed GM can reduce computational cost and suboptimize the RT-MPC online. Then, the conditions of the stability analysis and steady-state performance of the closed-loop systems are presented. Finally, numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance rejection performances. Experimental results demonstrate its effectiveness.

Efficient self-organizing multilayer neural network for nonlinear system modeling

It has been shown extensively that the dynamic behaviors of a neural system are strongly influenced by the network architecture and learning process. To establish an artificial neural network (ANN) with self-organizing architecture and suitable learning algorithm for nonlinear system modeling, an automatic axon-neural network (AANN) is investigated in the following respects. First, the network architecture is constructed automatically to change both the number of hidden neurons and topologies of the neural network during the training process. The approach introduced in adaptive connecting-and-pruning algorithm (ACP) is a type of mixed mode operation, which is equivalent to pruning or adding the connecting of the neurons, as well as inserting some required neurons directly. Secondly, the weights are adjusted, using a feedforward computation (FC) to obtain the information for the gradient during learning computation. Unlike most of the previous studies, AANN is able to self-organize the architecture and weights, and to improve the network performances. Also, the proposed AANN has been tested on a number of benchmark problems, ranging from nonlinear function approximating to nonlinear systems modeling. The experimental results show that AANN can have better performances than that of some existing neural networks.

A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network

The structure of RSONN can be self-organized based on the contributions of each hidden node, which uses not only the past states but also the current states.The appropriately adjusted learning rates of RSONN is derived based on the Lyapunov stability theorem. Moreover, the convergence of the proposed RSONN is discussed.An experimental hardware, including the proposed soft computing method is set up. The experimental results have confirmed that the soft computing method exhibits satisfactory predicting performance for SVI. In this paper, a soft computing method, based on a recurrent self-organizing neural network (RSONN) is proposed for predicting the sludge volume index (SVI) in the wastewater treatment process (WWTP). For this soft computing method, a growing and pruning method is developed to tune the structure of RSONN by the sensitivity analysis (SA) of hidden nodes. The redundant hidden nodes will be removed and the new hidden nodes will be inserted when the SA values of hidden nodes meet the criteria. Then, the structure of RSONN is able to be self-organized to maintain the prediction accuracy. Moreover, the convergence of RSONN is discussed in both the self-organizing phase and the phase following the modification of the structure for the soft computing method. Finally, the proposed soft computing method has been tested and compared to other algorithms by applying it to the problem of predicting SVI in WWTP. Experimental results demonstrate its effectiveness of achieving considerably better predicting performance for SVI values.