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Effects of supervised Self Organising Maps parameters on classification performance

ANALYTICA CHIMICA ACTA 2013年 765卷 45-53页

作者： Ballabio, Davide Vasighi, Mandi Filzmoser, Peter Univ Milano Bicocca Dept Environm Sci Milano Chemometr & QSAR Res Grp I-20126 Milan Italy IASBS Dept Comp Sci & Informat Technol Zanjan Iran Vienna Univ Technol Dept Stat & Probabil Theory A-1040 Vienna Austria

Self Organising Maps (SOMs) are one of the most powerful learning strategies among neural networks algorithms. SOMs have several adaptable parameters and the selection of appropriate network architectures is required in order to make accurate predictions. The major disadvantage of SOMs is probably due to the network optimisation, since this procedure can be often time-expensive. Effects of network size, training epochs and learning rate on the classification performance of SOMs are known, whereas the effect of other parameters (type of SOMs, weights initialisation, training algorithm, topology and boundary conditions) are not so obvious. This study was addressed to analyse the effect of SOMs parameters on the network classification performance, as well as on their computational times, taking into consideration a significant number of real datasets, in order to achieve A comprehensive statistical comparison. Parameters were contemporaneously evaluated by means of an approach based on the design of experiments, which enabled the investigation of their interaction effects. Results highlighted the most important parameters which influence the classification performance and enabled the identification of the optimal settings, as well as the optimal architectures to reduce the computational time of SOMs. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Self Organising Maps Kohonen maps Classification training algorithm SOMs parameters

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Radar Signal Recognition Based on CSRDNN Network

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IEEE ACCESS 2024年 12卷 86704-86715页

作者： Zhang, Zheng Wan, Chuan Chen, Yi Zhou, Fang Zhu, Xiaofei Zhai, Wenchao Quan, Daying China Jiliang Univ Sch Informat Engn Hangzhou 310018 Peoples R China Xian Res Inst High Technol Xian 710025 Peoples R China

It is essential to achieve the high-accuracy recognition of low probability of intercept (LPI) radar signals in modern electronic warfare. However, under low signal-to-noise ratio (SNR), the recognition accuracy of the LPI radar signals is relatively low. In this paper, a novel radar signal recognition method based on Convolutional Stacked Recurrent Deep Neural Network (CSRDNN) is proposed. Firstly, we design a Convolutional Neural Network (CNN) to expand the feature space of input time domain signals, the features extracted by CNN were then used as inputs of the Stacked Recurrent Neural Networks (SRNN) module. In the SRNN module, we sequentially stack GRU, LSTM, and BGRU, enabling the model to better handle the short-term and long-term dependence of signal features and effectively solve asynchronous problems in unidirectional RNN networks. Subsequently, a Fully Connected Deep Neural Network (FCDNN) was employed to accomplish the recognition task. In addition, we design a training algorithm composed of the Nesterov-Adaptive Moment Estimation (Nadam) algorithm and the CosineAnnealing Learning Rate (LR) adjustment strategy to improve the training efficiency of the model. The experimental results demonstrate that the proposed model has higher recognition accuracy at low SNR compared to other models, with an overall recognition accuracy of 92.96% at -4 dB.

关键词： Long short term memory training data Logic gates Recurrent neural networks Feature extraction Radar signal processing algorithm design and analysis Low probability of intercept radar signal recognition stacked recurrent neural networks activation function training algorithm

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Robust optimization of ANFIS based on a new modified GA

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NEUROCOMPUTING 2015年 166卷 357-366页

作者： Sarkheyli, Arezoo Zain, Azlan Mohd Sharif, Safian Univ Teknol Malaysia Fac Comp Soft Comp Res Grp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Mech Engn Dept Mfg & Ind Engn Skudai 81310 Johor Malaysia

Adaptive Network-based Fuzzy Inference Systems (ANFIS) is one of the most well-known predictions modeling technique utilized to find the superlative relationship between input and output parameters in different processes. training the adaptive modeling parameters in ANFIS is still a challengeable problem which has been recently considered by researchers. Hybridizing of a robust optimization algorithm with ANFIS as its training algorithm provides a scope to improve the effectiveness of membership functions and fuzzy rules in the model. In this paper, a new Modified Genetic algorithm (MGA) by using a new type of population is proposed to optimize the modeling parameters for membership functions and fuzzy rules in ANFIS. As well, a case study on a machining process is considered to illustrate the robustness of the proposed training technique in prediction of machining performances. The prediction results have demonstrated the superiority of the presented hybrid ANFIS-MGA in term of prediction accuracy (with 97.74%) over the other techniques such as hybridization of ANFIS with Genetic algorithm (GA), Taguchi-GA, Hybrid Learning algorithm (HL), Leave-One-Out Cross-Validation (LOO-CV), Particle Swarm Optimization (PSO) and Grid Partition method (GP), as well as RBFN and basic Grid Partition Method (GPM). In addition, an attempt is done to specify the effectiveness of different improvement rates on the prediction result and measuring the number of function evaluations required. The comparison result reveals that MGA with improvement rate 0.8 raises the convergence speed and accuracy of the prediction results compared to GA. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Adaptive network-based fuzzy inference systems (ANFIS) Modified genetic algorithm (MGA) training algorithm Population Prediction model Prediction accuracy Improvement rate

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A class of gradient unconstrained minimization algorithms with adaptive stepsize

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2000年第2期114卷 367-386页

作者： Vrahatis, MN Androulakis, GS Lambrinos, JN Magoulas, GD Univ Patras Artificial Intelligence Res Ctr Dept Math GR-26110 Patras Greece Univ Athens Dept Informat GR-15771 Athens Greece

In this paper the development, convergence theory and numerical testing of a class of gradient unconstrained minimization algorithms with adaptive stepsize are presented. The proposed class comprises four algorithms: the first two incorporate techniques for the adaptation of a common stepsize for all coordinate directions and the other two allow an individual adaptive stepsize along each coordinate direction. All the algorithms are computationally efficient and possess interesting convergence properties utilizing estimates of the Lipschitz constant that are obtained without additional function or gradient evaluations. The algorithms have been implemented and tested on some well-known test cases as well as on real-life artificial neural network applications and the results have been very satisfactory. (C) 2000 Elsevier Science B.V. All rights reserved. MSC: 65K05;65K10;49D37;65C20;82C32;68T05.

关键词： unconstrained optimization steepest descent gradient method Lipschitz constant line search strategies Armijo's method globally convergent method artificial neural network training algorithm

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Operational parameter impact and back propagation artificial neural network modeling for phosphate adsorption onto acid-activated neutralized red mud

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JOURNAL OF MOLECULAR LIQUIDS 2016年第0期216卷 35-41页

作者： Ye, Jie Cong, Xiangna Zhang, Panyue Zeng, Guangming Hoffmann, Erhard Wu, Yan Zhang, Haibo Fang, Wei Hunan Univ Coll Environm Sci & Engn Changsha 410082 Hunan Peoples R China Hunan Univ Minist Educ Key Lab Environm Biol & Pollut Control Changsha 410082 Hunan Peoples R China Karlsruhe Inst Technol Dept Aquat Environm Engn D-76131 Karlsruhe Germany Karlsruhe Inst Technol Inst Appl Mat IAM WK D-76131 Karlsruhe Germany

In this research the combination of neutralization activation and acid activation processes was employed to improve the physicochemical characters of red mud. In order to better understand the phosphate adsorption behaviors and further improve the phosphate adsorption performance of acid-activated neutralized red mud (AaN-RM), for the first time the impact of operational parameters on phosphate adsorption onto AaN-RM was systematically investigated, and back propagation artificial neural network (ANN) modeling was conducted. The results demonstrated that phosphate adsorption capacity of AaN-RM decreased with the enhancement of adsorbent dosage and the concentration of the competing ion (carbonate), while it increased with the increase of initial phosphate concentration and contact time. The optimal adsorption temperature and initial solution pH for phosphate adsorption onto AaN-RM were 50 degrees C and 4.0, respectively. Moreover, a 6-10-1 feed forward ANN structure with trainlm algorithm was successfully constructed for predicting the phosphate removal by AaN-RM. The RMSE and R-2 values for two subsets (training and validation subset, and testing subset) were 3.06 and 2.61, and 0.9932 and 0.9969, respectively. Furthermore, the importance analysis showed that contact time and initial phosphate concentration were the most influential parameters on phosphate removal by AaN-RM, the importance of which reached 24.64% and 22.16%, respectively. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Phosphate adsorption Red mud Artificial neural network training algorithm Importance analysis

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Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases

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NEURAL PROCESSING LETTERS 2023年第1期55卷 171-191页

作者： Namasudra, Suyel Dhamodharavadhani, S. Rathipriya, R. Natl Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Periyar Univ Dept Comp Sci Salem India

The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.

关键词： Levenberg Marquardt Bayesian regularization Scaled conjugate gradient Forecasting training algorithm Regression

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A fuzzy process neural network model and its application in process signal classification

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NEUROCOMPUTING 2019年 335卷 1-8页

作者： Xu, Shaohua Liu, Kun Li, Xuegui Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China

Aiming at process signal classification combined with fuzzy decision rules, a fuzzy process neural network (FPNN) is proposed in this paper. The FPNN is structured with a process signal input layer, a fuzzy process neuron (FPN) hidden layer, a signal pattern layer, and a fuzzy decision output layer. The spatio-temporal aggregation in the FPN is taken as a generalized inner product operation, which can measure the fuzzy similarity of the distributed features among process signals and FPN uses an exponential fuzzy membership function as activation function. The FPNN selectively sums the output of the FPN hidden layer to the pattern layer according to the category of input signal. By linking a Takagi-Sugeno fuzzy classifier behind the pattern layer, direct classification of the process signals is achieved. The FPNN can improve the deficiencies of existing time-varying signal classification methods, such as the complete training data sets are required, and the information processing processes and algorithms are complicated. In this paper, the theoretical properties of the FPNN are analyzed, and the comprehensive learning algorithm for FPNNs is given. The discrimination of reservoir water flooding condition based on multi-channel well logging process signals was used as an example for experimental analysis;the results verify the validity of the model and algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Process signal classification Fuzzy decision Fuzzy process neural network training algorithm Small sample modeling

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A Probabilistic Process Neural Network and Its Application in ECG Classification

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IEEE ACCESS 2019年 7卷 50431-50439页

作者： Feng, Naidan Xu, Shaohua Liang, Yongquan Liu, Kun Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Shandong Peoples R China

A novel probabilistic process neural network (PPNN) model is proposed for the multi-channel time-varying signal classification problems with ambiguity and randomness distribution characteristics. This model was constructed from an input time-varying signal layer, a probabilistic process neuron (PPN) hidden layer, a pattern layer, and a Softmax classifier. The number of nodes in the input layer is the same as the number of time-varying signal input channels, which can realize the overall input of the time-varying signal. The PPN hidden layer performs the spatio-temporal aggregation operations for time-varying input signals and probabilistic outputs. The connection weight functions from the input layer to the hidden layer are represented by the typical samples or cluster center functions in different pattern subsets of the sample set, which the prior knowledge of signal categories is implicitly expressed by the morphological distribution features and combinational relations. A pattern layer selectively summed to the output of the PPN hidden layer using the categorical attributes of the connection weight function vector. And the Softmax classifier implements the probabilistic classification of time-varying signals. PPNN has the advantages of fewer model parameters, suitable for modeling small samples and integrating prior knowledge of signal categories. This paper develops the specific learning algorithms which synthesize dynamic timewarping, C-means clustering, and BP algorithm. The 12-lead electrocardiogram (ECG) signals for heart disease diagnosis were used for classification testing. The experimental results from 12-lead ECG signal across ten types of disease classification verify the effectiveness of the model and the proposed algorithm.

关键词： Dynamic signal classification Bayesian decision rules probabilistic process neural network training algorithm ECG signal classification

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Parameter by parameter algorithm for multilayer perceptrons

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NEURAL PROCESSING LETTERS 2006年第2期23卷 229-242页

作者： Li, YL Zhang, D Wang, KQ Hong Kong Polytech Univ Dept Comp Biometr Res Ctr UGC CRC Kowloon Hong Kong Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Biocomp Res Ctr Harbin 150006 Peoples R China

This paper presents a parameter by parameter (PBP) algorithm for speeding up the training of multilayer perceptrons (MLP). This new algorithm uses an approach similar to that of the layer by layer (LBL) algorithm, taking into account the input errors of the output layer and hidden layer. The proposed PBP algorithm, however, is not burdened by the need to calculate the gradient of the error function. In each iteration step, the weights or thresholds can be optimized directly one by one with other variables fixed. Four classes of solution equations for parameters of networks are deducted. The effectiveness of the PBP algorithm is demonstrated using two benchmarks. In comparisons with the BP algorithm with momentum (BPM) and the conventional LBL algorithms, PBP obtains faster convergences and better simulation performances.

关键词： BP algorithm with momentum layer by layer algorithm multilayer perceptrons parameter by parameter algorithm training algorithm

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Active Control of 3-D Irregular Building by using Energy Based Neuro-Controller

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ADVANCES IN STRUCTURAL ENGINEERING 2014年第6期17卷 837-849页

作者： Bigdeli, Y. Kim, D. Kunsan Natl Univ Dept Civil Engn Jeonbuk South Korea

In this paper, a methodology of active control of structures based on modal energy is extended to control the response of irregular structures. The nonlinearities of material and geometry are taken into account in the training algorithm. A three-dimensional (3-D) multi-story irregular building is employed to scrutinize the performance of the improved control strategy. The torsional and lateral responses of the structure and the dynamic behavior of actuators have been incorporated in the control model simultaneously. Thus, the lateral-torsional coupling as well as the structure-actuator interaction is considered. The incident angle of ground motion and the time delay of actuators are also included in the model. El-Centro 1940 earthquake is used to train the algorithm, and then five different earthquake records have been used to evaluate the trained control system. The results of controlled structural responses in each case proved that the proposed control algorithm may be promising in vibration control of 3-D irregular structures.

关键词： active control training algorithm actuator dynamic irregular building Neuro-controller

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