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Hierarchical wavelet packet fuzzy inference system for pattern classification and system identification

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2013年第1期44卷 109-126页

作者： Sharifi, A. Shoorehdeli, M. Aliyari Teshnehlab, M. Islamic Azad Univ Sci & Res Branch Dept Comp Tehran Iran KNT Univ Technol Mechatron Dept Tehran Iran KNT Univ Technol Dept Elect Engn Tehran Iran

This study presents a hierarchical Takagi-Sugeno-Kang type fuzzy system called hierarchical wavelet packet fuzzy inference system. In the proposed method, wavelet packet transform is applied on the input data to produce approximation and detail sub-bands of the input data and the output is used as the input vector of the proposed network. This network uses a hierarchical structure same as wavelet packet decomposition tree, in which adaptive network-based fuzzy inference system is used as sub-model. Also, gradient descent algorithm is chosen for training the parameters of antecedent and conclusion parts of the sub-models. In order to evaluate the capability of the proposed method, its applications in pattern classification, system identification and time-series prediction have been studied. The results show that the proposed method performs better than the other conventional models.

关键词： wavelet transform hierarchical fuzzy model gradient descent algorithm pattern classification system identification time-series prediction

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Time-series prediction using a local linear wavelet neural network

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NEUROCOMPUTING 2006年第4-6期69卷 449-465页

作者： Chen, YH Yang, B Dong, JW Jinan Univ Sch Informat Sci & Engn Jinan 250022 Peoples R China Wuhan Univ Sci & Technol State Key Lab Adv Technol Mat Synthesis & Proc Wuhan Peoples R China

A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of particle swarm optimization (PSO) with diversity learning and gradient descent method is introduced for training the LLWNN. Simulation results for the prediction of time-series show the feasibility and effectiveness of the proposed method. (c) 2005 Elsevier B.V. All rights reserved.

关键词： local linear wavelet neural networks particle swarm optimization algorithm gradient descent algorithm time-series prediction

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Dynamic electricity price forecasting using local linear wavelet neural network

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NEURAL COMPUTING & APPLICATIONS 2015年第8期26卷 2039-2047页

作者： Pany, Prasanta Kumar Ghoshal, S. P. DRIEMS Cuttack Odisha India NIT Durgapur W Bengal India

Price forecasting has become one of the main focuses of electric power market research efforts as price is the key index to evaluate the market competition efficiency and reflects the operation condition of electricity market decision making. The work presented in this paper makes use of local linear wavelet neural networks to find the market clearing price for a given period, which is based on similar days approach. The results obtained through simulation are compared to other evolutionary optimization techniques surfaced in the recent state-of-the-art literature, including wavelet neural network model. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness for electricity price forecasting.

关键词： Electricity price Forecast Wavelet neural network (WNN) Local linear wavelet neural network gradient descent algorithm Market clearing price Weekly mean absolute percentage error (WMAPE)

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Prediction of PM2.5 concentration based on weighted bagging and image contrast-sensitive features

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2020年第3-4期34卷 561-573页

作者： Qiao, Junfei He, Zengzeng Du, Shengli Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

In this paper, we propose an effective method of PM2.5 prediction based on image contrast-sensitive features and weighted bagging based neural network (WBBNN). Three types of image contrast-sensitive features are first extracted from the images and fuzzified. Next, a weighted bagging strategy combining the ensemble fuzzy neural network (FNN) and ensemble radial basis function neural network (RBFNN) is established. The ensemble neural network (NN), regardless of FNN and RBFNN, is obtained by simply averaging the outputs of component neural networks. And these component neural networks are trained by the improved gradient descent algorithm and samples acquired by bootstrap sampling. Finally, the WBBNN is used to forecast PM2.5 concentration by extracting three types of image contrast-sensitive features. Results of experiments demonstrate that our prediction method is more reliable, practical and efficient than FNN, RBFNN, the ensemble NNs, and state-of-the-art quality assessment method in terms of predicting the concentration of PM2.5 More importantly, an improved gradient descent algorithm is developed to accelerate the convergence speed and ensure the prediction accuracy of WBBNN and the fuzzy features acquired by the feature fuzzification method can greatly improve the robustness and precision of WBBNN.

关键词： PM2.5 prediction Weighted bagging strategy Bootstrap sampling Contrast-sensitive feature Ensemble neural network gradient descent algorithm

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Self-Configuring and Reconfigurable Silicon Photonic Signal Processor

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ACS PHOTONICS 2020年第3期7卷 792-799页

作者： Zhou, Hailong Zhao, Yuhe Wang, Xu Gao, Dingshan Dong, Jianji Zhang, Xinliang Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Sch Opt & Elect Informat Wuhan 430074 Peoples R China Hong Kong Polytech Univ Photon Res Ctr Dept Elect & Informat Engn Hong Kong Peoples R China

Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, a self-configuring and fully reconfigurable silicon photonic signal processor is proposed and experimentally demonstrated. The proposed photonic signal processor is capable of performing various functions, including multichannel optical switching, optical multiple-input-multiple-output descrambler, and tunable optical filter. All the functions are achieved by complete self-configuration without knowing the inner structure. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with the self-configuring ability.

关键词： silicon photonics optical signal processing gradient descent algorithm programmable photonics

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Robust adaptive fuzzy control of twin rotor MIMO system

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SOFT COMPUTING 2013年第10期17卷 1847-1860页

作者： Jahed, Maryam Farrokhi, Mohammad Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

This paper considers designing an adaptive fuzzy controller to position the yaw and pitch angles of a twin rotor MIMO system (TRMS) in two degrees of freedom. The goal of the controller is to stabilize the TRMS in a desired position or track a specified trajectory. The parameters of the fuzzy controller are updated using the gradient descent algorithm in order to increase its robustness against external disturbances and/or changes in system parameters. Moreover, the stability of the overall closed-loop system is guaranteed based on the Lyapunov stability theory. The proposed controller is applied to a TRMS with heavy cross coupling between its axes. Experimental results show good performance of the proposed controller as compared to the non-adaptive fuzzy and PID controllers, especially when there are system uncertainties and external disturbances.

关键词： Adaptive fuzzy controller Twin rotor MIMO system gradient descent algorithm Lyapunov stability

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A hybrid PSO-GD based intelligent method for machine diagnosis

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DIGITAL SIGNAL PROCESSING 2006年第4期16卷 402-418页

作者： Guo, Qian-jin Yu, Hai-bin Xu, Ai-dong Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China

This paper presents an intelligent methodology for diagnosing incipient faults in rotating machinery. In this fault diagnosis system, wavelet neural network techniques are used in combination with a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of the constriction factor approach for particle swarm optimization (PSO) technique and the gradient descent (GD) technique, and is thus called HGDPSO. The HGDPSO is developed in such a way that a constriction factor approach for particle swarm optimization (CFA for PSO) is applied as a based level search, which can give a good direction to the optimal global region, and a local search gradient descent (GD) algorithm is used as a fine tuning to determine the optimal solution at the final. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity. (C) 2005 Elsevier Inc. All rights reserved.

关键词： wavelet neural network particle swarm optimization gradient descent algorithm machine diagnosis

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Form-finding method for multi-mode tensegrity structures using extended force density method by grouping elements

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COMPOSITE STRUCTURES 2018年 187卷 1-9页

作者： Cai, Jianguo Wang, Xinyu Deng, Xiaowei Feng, Jian Southeast Univ Natl Prestress Engn Res Ctr Minist Educ Key Lab C & PC Struct Nanjing 210096 Jiangsu Peoples R China Univ Hong Kong Dept Civil Engn Hong Kong Hong Kong Peoples R China

Multistable tensegrity structures are an intriguing form of compliant prestressed structures. Due to their attractive properties, these structures are attractive for a wide range applications. This papers aims the form-finding problem of tensegrity structures with multiple equilibrium modes. An optimization method for form-finding of multi-mode tensegrity structures is applied and then an equivalent optimization problem of energy-based objective function with Lagrange multiplier, regarded as an extension of the original force density method, is established. After the structural elements are grouped according to the property of symmetry, the objective function is minimized by the gradient descent algorithm, and as a result, the lengths of cable as well as the nodal coordinates are obtained and different structural modes corresponding to different grouping conditions can be achieved. Finally, several different modes in different grouping conditions of a planar and a spatial tensegrity structure have been obtained to verify the efficiency of proposed method.

关键词： Form-finding Multi-mode Energy method gradient descent algorithm Grouping

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Design of dynamic neural observers

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IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS 2000年第3期147卷 257-266页

作者： Ahmed, MS Riyaz, SH Daimler Chrysler Corp Auburn Hills MI 48326 USA King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia

A design of nonlinear-dynamic observer is proposed for determining the states of a nonlinear system. The design method uses a multi-layered feed-forward neural network (MFNN) to approximate the nonlinear Kalman gain. Two different criteria are proposed for the network training. The training is based on a gradient descent algorithm that uses block partial derivatives. Simulation results on Van der Pol's equation and the classical inverted pendulum model are presented to validate the usefulness of the scheme.

关键词： block partial derivatives nonlinear systems Learning in AI Simulation, modelling and identification observers classical inverted pendulum model dynamic neural observers nonlinear Kalman gain Neural nets Kalman filters network training Van der Pol's equation Neural computing techniques multilayer perceptrons feedforward neural nets gradient descent algorithm learning (artificial intelligence) multi-layered feedforward neural network nonlinear dynamic observer Signal processing theory

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Decoupling control for wastewater treatment process based on recurrent fuzzy neural network

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ASIAN JOURNAL OF CONTROL 2019年第3期21卷 1270-1280页

作者： Qiao, Jun-Fei Han, Gai-Tang Han, Hong-Gui Yang, Cui-Li Li, Wei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

Due to the characteristics of strong coupling and high nonlinearity in the control process, an intelligent decoupling control strategy based on recurrent fuzzy neural network (RFNN) is proposed in this paper to control the wastewater treatment process (WWTP). Firstly, the architecture of the RFNN controller is designed with a mechanism analysis of WWTP. Secondly, a decoupling strategy in combination with a gradient descent search algorithm is used to decouple the control loop of dissolved oxygen (D-O) concentration and nitrate nitrogen (S-NO) concentration. Finally, stability analysis based on a Lyapunov function is investigated. The proposed approach has been applied to the WWTP simulation model. Compared to model predictive control, echo state network-based HDP (E-HDP), conventional RFNN, and neural network on-line modelling and controlling methods, the proposed method has better control performance.

关键词： Wastewater treatment process decoupling control RFNN gradient descent algorithm

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