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nonlinear system modeling Using RBF Networks for Industrial Application

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第3期14卷 931-940页

作者： Meng, Xi Rozycki, Pawel Qiao, Jun-Fei Wilamowski, Bogdan M. Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Informat Technol & Management PL-35225 Rzeszow Poland Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

Radial basis function (RBF) networks, because of their universal approximation ability, have been widely applied to industrial process modeling. In this study, an Improved ErrCor (IErrCor) algorithm-an extension of error correction (ErrCor) algorithm-is proposed, in which compact structure and satisfactory generalization ability can be obtained with only one learning try. First, a second-order-based constructive mechanism guarantees the structure compactness and computational efficiency. Second, different from other algorithms that start with random or constant parameters, optimal initial parameters accelerate the convergence process and improve the convergence performance, making the IErrCor RBF network more stable. Convergence analysis is given to demonstrate and prove the reasonability and effectiveness of the proposed algorithm. Finally, the IErrCor algorithm has been evaluated and compared with several popular advanced learning algorithms such as support vector machine (SVM), extreme learning machine (ELM), and original ErrCor algorithm through a series of benchmark experiments and then been applied to effluent water quality prediction in wastewater treatment process. All the simulation results reveal the outperformance and potentiality of IErrCor RBF network in industrial applications.

关键词： Advanced learning algorithms improved error correction (IErrCor) nonlinear system modeling radial basis function (RBF) networks wastewater treatment process

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nonlinear system modeling and damping implementation of a boring bar

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第1-4期104卷 921-930页

作者： Li, Lie Sun, Beibei Hua, Haitao Southeast Univ Sch Mech Engn Nanjing 211189 Jiangsu Peoples R China

Vibration is a concern in the boring process due to the low dynamic stiffness of long cantilever boring bars. Vibration has a negative impact on the processing quality and processing performance. Dynamic vibration absorbers (DVAs) are used to consume vibration energy to improve the performance of boring operations. In this study, a nonlinear model is established to study the dynamic characteristics of a boring bar. Due to the consideration of nonlinear factors, the amplitude-frequency characteristics of the boring bar appear as an interesting phenomenon. As the damping of the DVA has an important influence on the vibration amplitude of the boring bar, the engineering implementation of this damping is conducted. Since the damping oil fills a hollow cylinder within the DVA, the length, inner diameter, and outer diameter of the hollow cylinder, along with the oil pressure and excitation frequency, are selected as parameters to study their influences on the damping value.

关键词： nonlinear system modeling Boring bar Dynamic vibration absorber Damping implementation

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nonlinear system modeling and identification using Volterra-PARAFAC models

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2012年第1期26卷 30-53页

作者： Favier, Gerard Kibangou, Alain Y. Bouilloc, Thomas Univ Nice Sophia Antipolis CNRS Lab I3S F-06903 Sophia Antipolis France Univ Grenoble 1 Syst Control Dept Gipsa Lab CNRS F-38402 St Martin Dheres France

Discrete-time Volterra models are widely used in various application areas. Their usefulness is mainly because of their ability to approximate to an arbitrary precision any fading memory nonlinear system and to their property of linearity with respect to parameters, the kernels coefficients. The main drawback of these models is their parametric complexity implying the need to estimate a huge number of parameters. Considering Volterra kernels of order higher than two as symmetric tensors, we use a parallel factor (PARAFAC) decomposition of the kernels to derive Volterra-PARAFAC models that induce a substantial parametric complexity reduction. We show that these models are equivalent to a set of Wiener models in parallel. We also show that Volterra kernel expansions onto orthonormal basis functions (OBF) can be viewed as Tucker models that we shall call Volterra-OBF-Tucker models. Finally, we propose three adaptive algorithms for identifying Volterra-PARAFAC models when inputoutput signals are complex-valued: the extended complex Kalman filter, the complex least mean square (CLMS) algorithm and the normalized CLMS algorithm. Some simulation results illustrate the effectiveness of the proposed identification methods. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： CLMS algorithm extended complex Kalman filter nonlinear system identification nonlinear system modeling PARAFAC tensor decompositions Volterra kernels Volterra models Wiener models

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nonlinear system modeling using self-organizing fuzzy neural networks for industrial applications

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APPLIED INTELLIGENCE 2020年第5期50卷 1657-1672页

作者： Zhou, Hongbiao Zhao, Huanyu Zhang, Yu Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China

In this paper, a novel self-organizing fuzzy neural network with an adaptive learning algorithm (SOFNN-ALA) for nonlinear system modeling and identification in industrial processes is proposed. To efficiently enhance the generalization capability, the proposed SOFNN-ALA is designed by using both structure identification and parameter estimation simultaneously in the learning process. In the structure identification phase, the rule neuron with the highest neuronal activity will be split into two new rule neurons. Meanwhile, the redundant rule neurons with small singular values will be removed to simplify the network structure. In the parameter estimation phase, an adaptive learning algorithm (ALA), which is designed based on the widely used Levenberg-Marquardt (LM) optimization algorithm, is adopted to optimize the network parameters. The ALA-based learning algorithm can not only speed up the convergence speed but also enhance the modeling performance. Moreover, we carefully analyze the convergence of the proposed SOFNN-ALA to guarantee its successful practical application. Finally, the effectiveness and efficiency of the proposed SOFNN-ALA is validated by several examples. The experimental results demonstrate that the proposed SOFNN-ALA exhibits a better comprehensive performance than some other state-of-the-art SOFNNs for nonlinear system modeling in industrial applications. The source code can be downloaded from https://***/hyitzhb/***.

关键词： nonlinear system modeling Self-organizing fuzzy neural network Neuronal activity Singular value decomposition Adaptive learning algorithm

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nonlinear system modeling using a self-organizing recurrent radial basis function neural network

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APPLIED SOFT COMPUTING 2018年 71卷 1105-1116页

作者： Han, Hong-Gui Guo, Ya-Nan Qiao, Jun-Fei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing Peoples R China

In this paper, an efficient self-organizing recurrent radial basis function neural network (RRBFNN), is developed for nonlinear system modeling. In RRBFNN, a two-steps learning approach is introduced during the learning process. In the first step, the objective is to find the optimal set of parameters using an improved Levenberg-Marquardt (LM) algorithm. In the second step, an efficient information-oriented algorithm (IOA), without any thresholds, is developed to optimize the structure of RRBFNN. The hidden neurons in this IOA-based RRBFNN (IOA-RRBFNN) are generated or pruned automatically to reduce the computational complexity and improve the generalization power. Meanwhile, a theoretical analysis on the learning convergence of IOA-RRBFNN is given in details. To demonstrate the merits of IOA-RRBFNN for modeling nonlinear systems, several benchmark problems and a real world application are present with comparisons against other existing methods. Some promising results are reported in this study, indicating that the proposed IOA-RRBFNN performs prediction accuracy in the case of fast learning speed and compact structure. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Information-oriented algorithm Recurrent radial basis function neural network nonlinear system modeling Improved Levenberg-Marquardt algorithm

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nonlinear system modeling and application based on restricted Boltzmann machine and improved BP neural network

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APPLIED INTELLIGENCE 2021年第1期51卷 37-50页

作者： Qiao, Junfei Wang, Longyang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

Aiming at the complexity, nonlinearity and difficulty in modeling of nonlinear system. In this paper, an improved back-propagation(BP) neural network based on restricted boltzmann machine(RBM-IBPNN) is proposed for nonlinear systems modeling. First, the structure of BP neural network(BPNN) is optimized by using sensitivity analysis(SA) and mutual information(MI) of the hidden neurons. Namely when the SA value and the MI value of the hidden neurons satisfy the set standard, the corresponding neurons will be pruned, split or merged. second, the restricted boltzmann machine(RBM) is employed to perform parameters initialization of training on the IBPNN. Finally, the proposed RBM-IBPNN is evaluated on nonlinear system identification, lorenz chaotic time series prediction and the total phosphorus prediction problems. The experimental results demonstrate that the proposed RBM-IBPNN not only has faster convergence speed and higher prediction accuracy, but also realizes a more compact network structure.

关键词： BP neural network Sensitivity analysis Mutual information Restricted boltzmann machine nonlinear system modeling

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nonlinear system modeling via Hybrid system Representation of Recurrent Fuzzy systems

Nonlinear System Modeling via Hybrid System Representation o...

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2010 IEEE World Congress on Computational Intelligence

作者： Schwung, Andreas Adamy, Juergen Tech Univ Darmstadt Inst Automat Control Control Theory & Robot Lab D-64283 Darmstadt Germany

ISBN: (纸本)9781424469208

This paper proposes a new approach to system modeling using continuous-time recurrent fuzzy systems (CTRFS). The approach is based on the representation of CTRFS as hybrid systems. With this representation, various forms of a priori knowledge about the system to be modeled can be incorporated. This allows a reasonable reduction of optimization parameters and hence, avoids overfitting. Furthermore, the presented approach offers a deeper insight into the system structure on the basis of measurement data solely. This is illustrated by a selection algorithm for relevant input and state variables. The applicability of the approach is shown by modeling a chemical process.

关键词： chemical process continuous time systems continuous-time recurrent fuzzy systems fuzzy set theory fuzzy systems hybrid system representation identification nonlinear control systems nonlinear dynamical systems nonlinear system modeling optimisation optimization parameters system structure

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Design of a self-organizing reciprocal modular neural network for nonlinear system modeling

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NEUROCOMPUTING 2020年 411卷 327-339页

作者： Li, Wenjing Li, Meng Zhang, Junkai Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Adv Innovat Ctr Future Internet Technol Beijing 100124 Peoples R China

Aiming to improve the model's generalization performance for nonlinear system modeling, a self organizing reciprocal modular neural network (SORMNN) is proposed in the present study, which imitates the modular structure with inter-module connections observed in human brains. The inter module connections in SORMNN are built by inputting the output of each subnetwork to other subnetworks. All subnetworks work in parallel to process the allocated features, and the structure of each subnetwork is designed to be self-organized by using a growing and pruning algorithm based on the contribution of hidden neurons. An improved Levenberg-Marquardt (LM) algorithm using a sliding window is conducted to update the parameters of SORMNN, which makes SORMNN available for solving online problems. To validate the effectiveness of the proposed model, SORMNN is tested on chaotic benchmark time series prediction, four UCI benchmark problems and a practical problem for biochemical oxygen demand prediction in wastewater treatment process. Experimental results demonstrate that SORMNN exhibits both a higher training accuracy and a better generalization ability for nonlinear system modeling than other modular neural networks, and the inter-module connections have a positive effect on the superior performance of the proposed model and can make the network structure compact. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Reciprocal modular neural network Inter-module connection Self-organization nonlinear system modeling

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An adaptive second order fuzzy neural network for nonlinear system modeling

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NEUROCOMPUTING 2016年 214卷 837-847页

作者： Han, Hong-Gui Ge, Lu-Ming Qiao, Jun-Fei Beijing Univ Technol Coll Elect & Control Engn Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

In this paper, an adaptive second order algorithm (ASOA) has been developed to train the fuzzy neural network (FNN) to achieve fast and robust convergence for nonlinear system modeling. Different from recent studies, this ASOA-based FNN (ASOA-FNN) has the quasi Hessian matrix and gradient vector which are accumulated as the sum of related sub matrices and vectors, respectively. Meanwhile, the learning rate of ASOA-FNN is designed to accelerate the learning speed. In addition, the convergence of the proposed ASOA-FNN has been proved both in the fixed learning rate phase and the adaptive learning rate phase. Finally, several comparisons have been realized and they have shown that the proposed ASOA-FNN has faster convergence speed and more accurate results than that of some existing methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： nonlinear system modeling Fuzzy neural network Adaptive second-order algorithm Fast convergence

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Differential evolution- based nonlinear system modeling using a bilinear series model

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APPLIED SOFT COMPUTING 2012年第11期12卷 3401-3407页

作者： Chang, Wei-Der Shu Te Univ Dept Comp & Commun Kaohsiung 824 Taiwan

This paper presents a new modeling method for nonlinear dynamic systems based on using bilinear series model. Basically, bilinear model is an extension of infinite impulse response (IIR) filter and belongs to the recursive nonlinear system model, i.e., its past output signals will heavily affect the present output. This kind of model can efficiently approximate a large class of nonlinear systems with fewer parameters than other non-recursive models. To adjust the model kernels, we here adopt an evolutionary computation called the differential evolution (DE) algorithm. This algorithm is based on real-valued manipulations and has a good convergence property for finding the global solution or the near global solution of optimized problem. Design steps of DE-based nonlinear system modeling are clearly given in this study. Finally, two kinds of digital systems are illustrated to demonstrate the efficiency of the proposed method. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Differential evolution (DE) Bilinear series model nonlinear system modeling

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