Junfei Qiao

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.

A self-organizing fuzzy neural network and its applications to function approximation and forecast modeling

To solve the problem of conventional input-output space partitioning, a new learning algorithm for creating self-organizing fuzzy neural networks (SOFNN) is proposed, which automates structure and parameter identification simultaneously based on input-target samples. First, a self-organizing clustering approach is used to establish the structure of the network and obtain the initial values of its parameters, then a supervised learning method to optimize these parameters. Two specific implementations of the algorithm, including function approximation and forecast modeling of the wastewater treatment system, are developed, comprehensive comparisons are made with other approaches in both of the examples. Simulation studies demonstrate the presented algorithm is superior in terms of compact structure and learning efficiency.

On-line adaptive control for inverted pendulum balancing based on feedback-error-learning

A new on-line adaptive control scheme based on feedback-error-learning is proposed and applied to inverted pendulum balancing. The proposed adaptive controller for balancing consists of a conventional feedback controller (CFC) and a neural network feedforward controller (NNFC). In the NNFC, the feedback error signal is employed as input stimulator, instead of the usual reference signal. An on-line back-propagation (BP) algorithm with the self-adaptive learning rate is developed and employed in the NNFC to realize the combination of learning and controlling. Computer simulations on inverted pendulum balancing task demonstrate that the proposed adaptive controller could effectively reduce precision requirements of the CFC parameters, and guarantees good balance performance and acceptable robust performance.

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.

Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications.