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A SHUNTING MULTILAYER PERCEPTRON NETWORK FOR CONFUSING COMPOSITE PATTERN-RECOGNITION

PATTERN RECOGNITION 1991年第11期24卷 1093-1103页

作者： WU, CH WANG, JF WU, WH NATL CHENG KUNG UNIV INST INFORMAT ENGNTAINANTAIWAN

In this paper, a shunting multilayer perceptron (SMLP) network trained with a modified backpropagation algorithm is proposed. In the SMLP, an MLP network shunts another MLP network in order to learn the relationship between any two training patterns. The SMLP network is endowed with two abilities in relation to patterns in complicated circumstances. Firstly, the SMLP network can perform confusing pattern recognition by weighting the node responses which are important for distinguishing two confusing patterns. Secondly, the SMLP network can perform composite pattern recognition by paying selective attention to the node responses which are distinctive for each individual pattern. The determination of the hidden node number for constructing a suitably sized SMLP network is also discussed here. For the experimental evaluations, ten spoken and handprinted digits and nine spoken and handprinted English alphabets were used. Comparisons to MLP, dynamic time warping algorithm, and nearest neighbor classifier showed satisfactory improvements in recognition accuracy for confusing/composite patterns.

关键词： NEURAL NETWORKS MULTILAYER PERCEPTRON backpropagation algorithm SPEECH PATTERN HANDPRINTED PATTERN CONFUSING COMPOSITE PATTERN

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PATTERN-RECOGNITION OF PARTIAL DISCHARGE IN XLPE CABLES USING A NEURAL NETWORK

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IEEE TRANSACTIONS ON ELECTRICAL INSULATION 1992年第3期27卷 543-549页

作者： SUZUKI, H ENDOH, T Power System Laboratory Hitachi Cable Limited Hitachi Japan

This paper describes an experimental study of pattern recognition of partial discharge (PD) in a crosslinked polyethylene (XLPE) cable by using a neural network (NN) system. The NN system was a three-layer artificial neural system with feedforward connections, and its learning method was a back-propagation algorithm incorporating an external teacher signal. Input information for the NN was a combination of the discharge magnitude, the number of pulse counts and the phase angle of applied voltage in which PD is produced. PD measurement was carried out using a PD pulse recorder for a 66 kV XLPE cable with an artificial defect under a 38 kV ac applied voltage. After learning 30 typical input patterns, the NN discriminated unknown patterns with 90% correct responses. The time duration including measuring time required for the NN to discriminate PD signal was approximately 30 s. In a long-term performance test of a 66 kV XLPE cable with an artificial defect, the NN-based alarm processor was able to recognize the presence of PD 1 h before breakdown of the cable, and successfully alerted the operator.

关键词： alarm systems cable insulation computerised pattern recognition high-voltage engineering insulation testing neural nets organic insulating materials partial discharges polymers power cables power engineering computing 66 kV XLPE cables alarm processor applied voltage artificial defect backpropagation algorithm discharge magnitude external teacher signal feedforward connections long-term performance test neural network number of pulse counts partial discharge phase angle Artificial neural networks backpropagation algorithms Cables Learning systems Neural networks Partial discharges Pattern recognition Polyethylene Pulse measurements Voltage

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An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection

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IEEE ACCESS 2023年 11卷 76330-76346页

作者： Al-Andoli, Mohammed Nasser Sim, Kok Swee Tan, Shing Chiang Goh, Pey Yun Lim, Chee Peng Multimedia Univ Fac Engn & Technol Melaka 75450 Malaysia Multimedia Univ Fac Informat Sci & Technol Melaka 75450 Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia

Digital networks and systems are susceptible to malicious software (malware) attacks. Deep learning (DL) models have recently emerged as effective methods to classify and detect malware. However, DL models often relies on gradient descent optimization in learning, i.e., the Back-Propagation (BP) algorithm;therefore, their training and optimization procedures suffer from several limitations, such as high computational cost and local suboptimal solutions. On the other hand, ensemble methods overcome the shortcomings of individual models by consolidating their strengths to increase performance. In this paper, we propose an ensemble-based parallel DL classifier for malware detection. In particular, a stacked ensemble learning method is developed, which leverages five DL base models and a neural network as a meta model. The DL models are trained and optimized with a hybrid optimization method based on BP and Particle Swarm Optimization (PSO) algorithms. To improve scalability and efficiency of the ensemble method, a parallel computing framework is exploited. The proposed ensemble method is evaluated using five malware datasets (namely, Drebin, NTAM, TOP-PE, DikeDataset, and ML_Android), and high accuracy rates of 99.2%, 99.3%, 98.7%, 100%, and 100% have been achieved, respectively. Its parallel implementation also significantly enhances the computational speed by a factor up to 6.75 times. These results ascertain that the proposed ensemble method is effective, efficient, and scalable, outperforming many other compared methods in malware detection.

关键词： Ensemble method malware detection deep learning parallel processing backpropagation algorithm particle swarm optimization

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ANN based Sensor Faults Detection, Isolation, and Reading Estimates - SFDIRE: Applied in a nuclear process

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ANNALS OF NUCLEAR ENERGY 2012年 49卷 131-142页

作者： Elnokity, Omar Mahmoud, Imbaby I. Refai, Mohamed K. Farahat, Hasan M. Al Azhar Univ Fac Engn Cairo Egypt Atom Energy Author NRC Cairo Egypt

In this paper a new method is developed based on the capabilities of the Artificial Neural Networks (ANNs) to detect, isolate, and output an estimated reading of faulty sensor/s reading/s that serves in monitoring a complex process. For a number of measured signals working to monitor a process, we allow training of an equal number of ANN's to those signals. Each ANN has an output signal equal to one corresponding measured signal and the set of inputs is the rest of other measured signals and other networks output signals using two-hidden layer feedforward network with different number of neurons for each. After the measuring the error between the real signal and the ANN output, the faulty sensor can be detected. The reading of this faulty sensor could be isolated and an estimated reading is sent to the monitoring system. This method could be applied for any difficult to model process to increase its availability and in the same time maintaining safety. In this paper, the application considered is the thermal-hydraulics process of Egyptian Training and Research Reactor-2 (ETRR-2) core cooling system. Data from the reactor are used to train the networks using backpropagation algorithm. An approximated model based on the lumped parameters approach for the thermal-hydraulics of the reactor core is built to compare results against ANN based system. The proposed system deals with 6 signals. From comparison, very good results are achieved when testing the design. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： ANN Artificial Neural Networks SFDIRE Feedforward Hidden layer backpropagation algorithm ETRR-2

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A novel fuzzy neural network based distance relaying scheme

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IEEE TRANSACTIONS ON POWER DELIVERY 2000年第3期15卷 902-907页

作者： Dash, PK Pradhan, AK Panda, G Reg Engn Coll Dept Elect Engn Rourkela India

This paper presents a nem approach to distance relaying using fuzzy neural network (FNN). The FNN can be viewed either as a fuzzy system, a neural network or fuzzy neural network. The structure is seen as a neural network for training and a fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The number of rules is determined by the data itself and therefore smaller number of rules is produced. The network is trained with back propagation algorithm. A pruning strategy is applied to eliminate the redundant rules and fuzzification neurons. consequently a compact structure is achieved. The classification and location tasks are accomplished by using different FNN's, Once the fault type is identified by the FNN classifier the selected fault locating FNN estimates the location of the fault accurately, Normalized peaks of fundamental voltage and current waveforms are considered as inputs to all the networks and an additional input derived from df component is fed to fault locating networks. The peaks and de component are extracted from sampled signals by the EKF, Test results show that the nem approach provides robust and accurate classification/location of faults for a variety of power system operating conditions even with resistance in the fault path.

关键词： backpropagation fault location fuzzy neural nets power system analysis computing power system faults power system protection relay protection DC component EKF backpropagation algorithm current waveform distance relaying scheme fault path resistance fault Fault location Power system analysis computing Power system faults backpropagation algorithms back propagation Fuzzy neural networks Relay protection Power system protection DC component

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Application of chaotic noise reduction techniques to chaotic data trained by ANN

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2001年第5期26卷 485-494页

作者： Bhat, CCS Kaimal, MR Ramamohan, TR CSIR Reg Res Lab Unit 1 Thiruvananthapuram 695019 Kerala India

We propose a novel method of combining artificial neural networks (ANNs) with chaotic noise reduction techniques that captures the metric and dynamic invariants of a chaotic time series, e.g. a time series obtained by iterating the logistic map in chaotic regimes. Our results indicate that while the feedforward neural network is capable of capturing the dynamical and metric invariants of chaotic time series within an error of about 25%, ANNs along with chaotic noise reduction techniques, such as Hammel's method or the local projective method, can significantly improve these results. This further suggests that the effort on the ANN to train data corresponding to complex structures can be significantly reduced. This technique can be applied in areas like signal processing, data communication, image processing etc.

关键词： backpropagation algorithm noise reduction logistic map

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PERFORMANCE OF THE GENERALIZED DELTA-RULE IN STRUCTURAL DAMAGE DETECTION

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 1995年第2期8卷 211-221页

作者： BARAI, SV PANDEY, PC INDIAN INST SCI DEPT CIVIL ENGNBANGALORE 560012KARNATAKAINDIA

The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.

关键词： ARTIFICIAL NEURAL NETWORKS (ANN) backpropagation algorithm BRIDGE STRUCTURE DAMAGE DETECTION FEM GENERALIZED DELTA RULE (GDR) MULTILAYER PERCEPTRONS NETWORK ARCHITECTURE STRUCTURAL IDENTIFICATION TESTING PATTERNS TRAINING PATTERNS

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Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2011年第3期15卷 1556-1566页

作者： Velazquez, Sergio Carta, Jose A. Matias, J. M. Univ Las Palmas Gran Canaria Dept Mech Engn Las Palmas Gran Canaria 35017 Canary Islands Spain Univ Las Palmas Gran Canaria Dept Elect & Automat Engn Las Palmas Gran Canaria 35017 Canary Islands Spain Univ Vigo Dept Stat Vigo 36200 Spain

In the scientific literature concerning renewable energies there have been proposals for the use of Artificial Neural Networks (ANNs) as a tool to estimate the mean wind speed at a target station for which only incomplete data series are available. In general terms, the wind speeds recorded at neighbouring reference stations are used as signals of the input layer of multilayer perceptron (MLP) architectures. An analysis is undertaken in this paper of the extent to which estimation of the wind speed, the wind power density and the power output of an installed wind turbine at a target site is affected by the number of reference stations, the degree of correlation between the wind speeds of the reference stations and the target station, the wind direction and the manner in which the direction signal is introduced into the input layer. For the purposes of this study mean hourly wind speeds and directions were used which had been recorded at twenty two weather stations located in the Canary Archipelago (Spain) in 2002 and 2006. The power-wind speed characteristic curves of five wind turbines of different rated power were also used. The MLP architectures were trained using the backpropagation algorithm. Amongst other conclusions reached with the tests performed with the set of data not used in the training nor in the validation of the models is that the estimation errors tend to decrease as the number of reference stations used increases, independently of the existing correlation coefficient values between the reference stations and the candidate station. It was also observed that if input signals of angular wind direction and wind speed are used then the number of reference stations required to achieve a certain decrease in the error is lower than the number required if only wind speed input signals are used. By using multiple reference stations with input signals of angular wind direction and speed, error reductions have been achieved for some stations in the estimation

关键词： Artificial Neural Networks Wind power estimation Wind direction Multilayer perceptron backpropagation algorithm Mean absolute relative error

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A review of artificial neural network models for ambient air pollution prediction

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ENVIRONMENTAL MODELLING & SOFTWARE 2019年 119卷 285-304页

作者： Cabaneros, Sheen Mclean Calautit, John Kaiser Hughes, Ben Richard Univ Strathclyde Dept Mech & Aerosp Engn Glasgow Lanark Scotland Univ Nottingham Dept Architecture & Built Environm Nottingham England

Research activity in the field of air pollution forecasting using artificial neural networks (ANNs) has increased dramatically in recent years. However, the development of ANN models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM10, PM2.5, and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models.

关键词： Air pollution Artificial neural networks Multilayer perceptron Forecasting backpropagation algorithm

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Re'current-fuzzy-neural-network-controlled linear induction motor servo drive using genetic algorithms

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2007年第3期54卷 1449-1461页

作者： Lin, Faa-Jeng Huang, Po-Kai Chou, Wen-Der Natl Dong Hwa Univ Dept Elect Engn Hualien 974 Taiwan Lan Yang Inst Technol Dept Informat Management Ilan 261 Taiwan

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive I'S derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance.

关键词： backpropagation algorithm genetic algorithms (GAs) linear induction motor (LIM) recurrent fuzzy neural network (RFNN)

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