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A Bird Flock Gravitational Search algorithm Based on the Collective Response of Birds

COMPUTER JOURNAL 2017年第11期60卷 1687-1716页

作者： Han, XiaoHong Qiang, Yan Lan, Yuan Taiyuan Univ Technol Minist Educ China Key Lab Adv Transducers & Intelligent Control Sys Taiyuan Shanxi Peoples R China

Gravitational search algorithm (GSA) is a stochastic search algorithm based on the law of gravity and mass which is widely used nowadays for efficient solution of optimization problems. For the purpose of enhancing the performance of original GSA, this paper proposes a new GSA called Bird Flock Gravitational Search algorithm (BFGSA) based on the collective response of birds. Although GSA performs well in many problems, algorithms in this category lack mechanisms which add diversity to exploration in the search process. Our proposed algorithm introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main major steps including initialization, identification of the nearest neighbors and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly composed benchmark functions presented in CEC2005 competition and the results are compared with recent variants of the original particle swarm optimization and state-of-the-art GSA algorithms. Furthermore, we applied BFGSA to a real-world application of data clustering. The results show that BFGSA improves the performance of the original GSA and obtains the best results compared with our selected GSA-type algorithms in benchmarking experiments and clustering experiments.

关键词： gravitational search algorithm learning algorithm heuristic search algorithm collective behavior numerical function optimization data clustering

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Generalization ability of fractional polynomial models

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NEURAL NETWORKS 2014年 49卷 59-73页

作者： Lei, Yunwen Ding, Lixin Ding, Yiming Wuhan Univ Sch Comp State Key Lab Software Engn Wuhan 430072 Peoples R China Chinese Acad Sci Wuhan Inst Phys & Math Wuhan 430071 Peoples R China

In this paper, the problem of learning the functional dependency between input and output variables from scattered data using fractional polynomial models (FPM) is investigated. The estimation error bounds are obtained by calculating the pseudo-dimension of FPM, which is shown to be equal to that of sparse polynomial models (SPM). A linear decay of the approximation error is obtained for a class of target functions which are dense in the space of continuous functions. We derive a structural risk analogous to the Schwartz Criterion and demonstrate theoretically that the model minimizing this structural risk can achieve a favorable balance between estimation and approximation errors. An empirical model selection comparison is also performed to justify the usage of this structural risk in selecting the optimal complexity index from the data. We show that the construction of FPM can be efficiently addressed by the variable projection method. Furthermore, our empirical study implies that FPM could attain better generalization performance when compared with SPM and cubic splines. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： learning algorithm learning theory Fractional polynomial Model selection Approximation theory

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An improved maximum spread algorithm with application to complex-valued RBF neural networks

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NEUROCOMPUTING 2016年 216卷 261-267页

作者： Liu, Yuanshan Huang, He Huang, Tingwen Qian, Xusheng Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China Texas A&M Univ Qatar Doha 5825 Qatar

It has been known that one of the important steps in training a complex-valued radial basis function neural network is to effectively determine its centers and widths of neurons in the hidden layer. In this paper, an improved maximum spread algorithm is propose to solve this issue. Its basic idea is that the choice of centers not only depends on the distances between samples from different classes, but also is heavily affected by the average distance between samples in the same class. The relationship between external and inner distances is taken into account when determining centers. The performance of this algorithm is tested on several datasets. It is shown that much better performance can be achieved by the developed algorithm than by some existing ones. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Complex-valued RBF neural networks learning algorithm Inner distance Classification

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A novel data clustering algorithm based on modified gravitational search algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 61卷 1-7页

作者： Han, XiaoHong Quan, Long Xiong, XiaoYan Almeter, Matt Xiang, Jie Lan, Yuan Taiyuan Univ Technol Key Lab Adv Transducers & Intelligent Control Sys Minist Educ China Taiyuan Shanxi Peoples R China

Data clustering is a popular analysis tool for data statistics in many fields such as pattern recognition, data mining, machine learning, image analysis, and bioinformatics. The aim of data clustering is to represent large datasets by a fewer number of prototypes or clusters, which brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. In this paper, a novel data clustering algorithm based on modified Gravitational Search algorithm is proposed, which is called Bird Flock Gravitational Search algorithm (BFGSA). The BFGSA introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main steps including initialization, identification of the nearest neighbors, and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The performance of the proposed algorithm is evaluated through 13 real benchmark datasets from the well-known UCI Machine learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly algorithm (FA), K-means, and other four clustering algorithms from the literature. The simulation results indicate that the BFGSA can effectively be used for data clustering.

关键词： Gravitational search algorithm learning algorithm Collective behavior Data clustering Clustering Validation Nature-inspired algorithm

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A color pattern recognition problem based on the multiple classes random neural network model

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NEUROCOMPUTING 2004年第1-4期61卷 71-83页

作者： Aguilar, J Univ Los Andes Fac Ingn Dept Computac CEMISID Merida Venezuela

Gelenbe has modeled the neural network using an analogy with the queuing theory. Recently, Fourneau and Gelenbe have proposed an extension of this model, called multiple classes random neural network (RNN) model. The purpose of this paper is to describe the use of the multiple classes RNN model to recognize patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon the non-linear equations of the multiple classes RNN model using gradient descent of a quadratic error function. In addition, we propose a progressive retrieval process with adaptive threshold value. (C) 2004 Elsevier B.V. All rights reserved.

关键词： multiple classes random neural network color pattern recognition learning algorithm image processing retrieval process

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Enhanced radial basis function neural networks for ozone level estimation

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NEUROCOMPUTING 2015年 155卷 62-70页

作者： Ha, Q. P. Wahid, H. Duc, H. Azzi, M. Univ Technol Sydney Fac Engn & Informat Technol Broadway NSW 2007 Australia Univ Teknol Malaysia Fac Elect Engn Skudai 81310 Malaysia Off Environm & Heritage Lidcombe NSW 1825 Australia CSIRO Energy Technol N Ryde NSW 1670 Australia

Assessment of air pollutant profiles by using measurements involves some limitations in the implementation. For this, deterministic air quality models are often used. However, its simulation usually needs high computational requirements due to complex chemical reactions involved. In this paper, a neural network-based metamodel approach is used in conjunction with a deterministic model and some measured data to approximate the non-linear ozone concentration relationship. For this, algorithms for performance enhancement of a radial basis function neural network (RBFNN) are developed. The proposed method is then applied to estimate the spatial distribution of ozone concentrations in the Sydney basin. The experimental comparison between the proposed RBFNN algorithm and the conventional RBFNN algorithm demonstrates the effectiveness and efficiency in estimating the spatial distribution of ozone level. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Ozone Radial basis function network learning algorithm Metamodel Spatial distribution

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Time series forecasting with a neuro-fuzzy modeling scheme

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APPLIED SOFT COMPUTING 2015年 32卷 481-493页

作者： Peng, Hung-Wen Wu, Shen-Fu Wei, Chia-Ching Lee, Shie-Jue Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 804 Taiwan

Time series forecasting concerns the prediction of future values based on the observations previously taken at equally spaced time points. Statistical methods have been extensively applied in the forecasting community for the past decades. Recently, machine learning techniques have drawn attention and useful forecasting systems based on these techniques have been developed. In this paper, we propose an approach based on neuro-fuzzy modeling for time series prediction. Given a predicting sequence, the local context of the sequence is located in the series of the observed data. Proper lags of relevant variables are selected and training patterns are extracted. Based on the extracted training patterns, a set of TSK fuzzy rules are constructed and the parameters involved in the rules are refined by a hybrid learning algorithm. The refined fuzzy rules are then used for prediction. Our approach has several advantages. It can produce adaptive forecasting models. It works for univariate and multivariate prediction. It also works for one-step as well as multi-step prediction. Several experiments are conducted to demonstrate the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Time series prediction Neuro-fuzzy modeling TSK fuzzy rules learning algorithm Training patterns

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Multiple Description Convolutional Neural Networks for Image Compression

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第8期29卷 2494-2508页

作者： Zhao, Lijun Bai, Huihui Wang, Anhong Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Taiyuan Univ Sci & Technol Inst Digital Media & Commun Taiyuan 030024 Peoples R China

Multiple description coding (MDC) is able to stably transmit signal in un-reliable and non-prioritized networks, which has been broadly studied for several decades. However, traditional MDC does not well leverage image's context features to generate multiple descriptions. In this paper, we propose a novel standard-compliant convolutional neural network-based MDC framework, which efficiently leverages image's context information to compress the image. First, multiple description generator network (MDGN) is designed to produce appearance-similar yet feature-different multiple descriptions automatically according to image's content, which are compressed by a standard codec. Second, we present multiple description reconstruction network (MDRN) including side reconstruction networks (SRNs) and central reconstruction network (CRN). When any one of two lossy descriptions is received at decoder, SRN network is used to improve the quality of this decoded lossy description by simultaneously removing compression artifact and up-sampling. Meanwhile, we utilize CRN network with two decoded descriptions as inputs for better reconstruction, if both of lossy descriptions are available. Third, multiple description virtual codec network is proposed to bridge the gap between MDGN network and MDRN network in order to train an end-to-end MDC framework. Here, two learning algorithms are provided to train our whole framework. In addition to structural dis-similarity loss function, the produced descriptions are used as opposing labels with multiple description distance loss function to regularize the training of MDGN network. These losses guarantee that the generated descriptions are structurally similar yet finely diverse. Experimental results show a great deal of objective and subjective quality measurements to validate the effectiveness of our framework.

关键词： Multiple description networks distance loss virtual codec learning algorithm coding artifacts

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The fractional correction rule: a new perspective

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NEURAL NETWORKS 1998年第6期11卷 1027-1039页

作者： Basu, M Liang, Q CUNY City Coll Dept Elect Engn New York NY 10031 USA CUNY City Coll Ctr Biomed Engn New York NY 10031 USA CUNY Grad Ctr New York NY 10031 USA

In this paper we discover and explore a useful property of the fractional correction rule, a variation of the perceptron learning rule, for a single neural unit. We rename this rule the projection learning rule (PrLR), which seems more appropriate because of the technique that we use to prove convergence. We state and prove a more powerful convergence theorem and establish the link with S. Agmon's work (1954) on linear inequalities. The hallmark of this rule is that if the problem is not linearly separable then the proposed rule will always converge to the origin of the weight-space. This points out that appropriate nonlinear methods (e.g., multilayer neural network, nonlinear transformation on input-space etc.) should be used to address this problem. On the other hand, if the patterns are linearly separable, the performance of this rule is equivalent to that of the perceptron rule. A theoretical investigation of this rule leads to interesting observations. We present experimental results with linearly separable as well as linearly non-separable data using the PrLR and compare its performance with that of the perceptron rule. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： learning algorithm neural network perceptron projection method supervised learning error correction

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Neural network based fault detection in robotic manipulators

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IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION 1998年第2期14卷 342-348页

作者： Vemuri, AT Polycarpou, MM Diakourtis, SA Univ Cincinnati Dept Elect & Comp Engn Cincinnati OH 45221 USA

Fault detection, diagnosis, and accommodation play a key role in the operation of autonomous and intelligent robotic systems. System faults, which typically result in changes in critical system parameters or even system dynamics, may lead to degradation in performance and unsafe operating conditions, This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators, A learning architecture, with neural networks as on-line approximators of the off-nominal system behavior, is used for monitoring the robotic system for faults. The approximation (by the neural network) of the off-nominal behavior provides a model of the fault characteristics which can be used for detection and isolation of faults. The stability and performance properties of the proposed fault detection scheme in the presence of system failure are rigorously established. Simulation examples are presented to illustrate the ability of the neural network based fault diagnosis methodology described in this paper to detect and accommodate faults in a simple two-link robotic system.

关键词： fault accommodation fault detection fault diagnosis learning algorithm neural networks robotic systems

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