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Neuro fuzzy schemes for fault detection in power transformer

APPLIED SOFT COMPUTING 2007年第2期7卷 534-539页

作者： Duraisamy, V. Devarajan, N. Somasundareswari, D. Vasanth, A. Antony Maria Sivanandam, S. N. Kumaraguru Coll Technol Dept Elect & Elect Engn Coimbatore 641006 Tamil Nadu India Govt Coll Technol Dept Elect & Elect Engn Coimbatore 641013 Tamil Nadu India PSG Coll Technol Dept Comp Sci & Engn Coimbatore 641004 Tamil Nadu India

This paper proposes fuzzy system and neural network approaches to identify the incipient faults in the power transformer using dissolved gas analysis ( DGA) method. Using the IEC/ IEEE DGA criteria and the gas concentration values as references the fuzzy diagnosis system and neural network are built. The proposed systems are verified using practical data collected from Electricity Board. The fuzzy system is tested with triangular, trapezoidal and Gaussian membership functions and its effectiveness is analyzed through simulation in terms of accuracy in identifying the transformer faults. The proposed back propagation network is verified to overcome the drawbacks of conventional methods. The proposed schemes are simulated and tested in the software environment. The simulation results are presented. (c) 2006 Elsevier B. V. All rights reserved.

关键词： transformer dissolved gas analysis fuzzy diagnosis system membership functions artificial neural network back propagation algorithm

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Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

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IEEE ACCESS 2020年 8卷 115167-115183页

作者： Ghazvini, Anahita Abdullah, Siti Norul Huda Sheikh Hasan, Mohammad Kamrul Bin Kasim, Zainal Abidin Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Malaysia Res & Dev Royal Malaysia Police Headquarters Inspector Gen Secretariat SAC Royal Malaysia Police Kuala Lumpur 50560 Malaysia

In the criminology area, to detain the serial criminal, the forthcoming serial crime time, distance, and criminal's biography are essential keys. The main concern of this study is on the upcoming serial crime distance, time, and suspect biographies such as age and nationality. In conjunction with having time delays, the dynamic classifier, like Time Delay Neural Network (TDNN) utilized to perform nonlinear techniques-based predictions. The TDNN classifier system, like back propagation Through Time (BPTT) and Nonlinear Autoregressive with Exogenous Input (NARX) are two prominent examples. However, BPTT and NARX techniques are unable to identify the dynamic system by using single-activation functions due to producing lower accuracy. Hence, during the training phase, the direct minimization of the TDNN error can further enhance the single activation function. Thus, this work introduces an enhanced NARX (eNARX) model based on the proposed activation functions of SiRBF via fusion of two functions of the hyperbolic tangent (Tansig) and Radial Basis Function (RBF), in the same hidden layer. If a fusion of activation functions can affect the TDNN error minimization, then fusing of the Tansig and RBF functions can produce a precise prediction for crime spatiotemporal. To evaluate the proposed technique and compared it with existing NARX and BPTT, we utilized five time-series datasets, namely, Dow Jones Index, Monthly River flow in cubic meters per second, Daily temperature, and UKM-PDRM datasets namely, "Suspect & Capture" and "Crime Plotting." The analysis of the results demonstrated that the proposed eNARX produce higher accuracy in comparison to other techniques of NARX and BPTT. Consequently, the proposed technique provides more effective results for the prediction of commercial serial crime.

关键词： Training Prediction algorithms Artificial neural networks Biological neural networks Delay effects Spatiotemporal phenomena back propagation algorithm enhanced NARX NARX neural network temporal pattern recognition quantum geographic information systems

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The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

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JOURNAL OF COMPUTATIONAL SCIENCE 2012年第1-2期3卷 46-55页

作者： de Mingo Lopez, Luis Fernando Gomez Blas, Nuria Arteta, Alberto Univ Politecn Madrid Escuela Univ Informat Madrid 28031 Spain

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Social intelligence Neural networks Grammatical swarm Particle swarm optimization back propagation algorithm

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A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年

作者： Bas, Eren Egrioglu, Erol Yolcu, Ufuk Giresun Univ Fac Arts & Sci Dept Stat Forecast Res Lab TR-28200 Giresun Turkey Univ Lancaster Mkt Analyt & Forecasting Res Ctr Management Sci Sch Dept Management Sci Lancaster England Giresun Univ Fac Adm & Management Sci Forecast Res Lab Dept Econometr TR-28200 Giresun Turkey

In the literature, the multiplicative neuron model artificial neural networks are trained by gradient-based or some artificial intelligence optimization algorithms. It is well known that the hybrid algorithms give successful results than classical algorithms in the literature and the use of hybrid systems increase day by day. From this point of view, different from other studies contribute to multiplicative neuron model artificial neural networks, the properties of an artificial intelligence optimization technique, artificial bat algorithm, and a gradient-based algorithm, backpropagation learning algorithm, is used together firstly by using the proposed method in this study. Thus, both a derivative and a heuristic algorithm were used together firstly for multiplicative neuron model artificial neural networks. The proposed method is applied to three well-known different real-world time series data. The performance of the proposed method is both compared with gradient-based optimization algorithms, some artificial optimization algorithms used for the training of artificial neural networks and some popular analyze methods. The analysis results show that the proposed hybrid method has superior performance than other methods.

关键词： Artificial bat algorithm back propagation algorithm Hybrid algorithm Multiplicative neuron model Artificial neural networks Forecasting

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Wavelet Coefficient Trained Neural Network Classifier for Improvement in Qualitative Classification Performance of Oxygen-Plasma Treated Thick Film Tin Oxide Sensor Array Exposed to Different Odors/Gases

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IEEE SENSORS JOURNAL 2011年第4期11卷 1013-1018页

作者： Kumar, Ravi Das, R. R. Mishra, V. N. Dwivedi, R. Banaras Hindu Univ Inst Technol Dept Elect Engn IT BHU Varanasi 221005 Uttar Pradesh India

A new soft computational approach for discrimination of odors/gases is presented. The proposed technique is applied on the raw data obtained from the responses of oxygen plasma treated thick film tin oxide sensor array exposed to four different odors/gases. The data generated from the sensor array response were subjected to wavelet transform and appropriate coefficients were selected using multiscale principal component analysis (MSPCA). The training and test performances of back-propagation trained neural network (BPNN) and radial basis function neural network (RBFNN) have been compared. Both the networks have been found to identify the odors/gases with a high success rate.

关键词： back propagation algorithm multiscale principal component analysis (MSPCA) oxygen plasma radial basis functions tin oxide sensors wavelet transform

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DAVION LEAST SQUARES-BASED LEARNING algorithm FOR FEEDFORWARD NEURAL NETWORKS

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NEURAL NETWORKS 1994年第4期7卷 661-670页

作者： KASPARIAN, V BATUR, C ZHANG, H PADOVAN, J UNIV AKRON DEPT MECH ENGNAKRONOH 44325

This paper presents a new learning methodology for feedforward neural networks. The proposed algorithm is based on Davidon's least squares minimization approach. The performance of the Davidon algorithm is compared with that of the back propagation for three prototype examples. These examples are: Case I, a linear second-order system;Case II, a chaotic system;and Case III, a nonlinear dynamic system. The trained network is employed to determine the one-step-ahead prediction of the output of a given system. The simulation results show that, in most cases, the Davidon algorithm has an order of magnitude faster convergence rate than that of the back propagation. In order to make a fair comparison, an optimum back propagation learning rate is used. The learning rate chosen is the one that results in the fastest convergence for a given system.

关键词： FEEDFORWARD NEURAL NETWORKS NETWORK TRAINING DAVIDON algorithm back propagation algorithm QUASINEWTON METHODS HESSIAN MATRIX CHAOTIC SYSTEM

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An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds

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ENVIRONMENTAL MODELLING & SOFTWARE 2007年第7期22卷 1066-1072页

作者： Iliadis, Lazaros S. Maris, Fotis Democritus Univ Thrace Dept Forestry & Management Environm & Nat Resourc Orestiada 68200 Thrace Greece

This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of mountainous water supplies. It proposes a model to be used effectively in the estimation of the average annual water supply, in each mountainous watershed of Cyprus. This is really a crucial task, especially during the long dry summer months of the island. On the other hand the evaluation of the potential torrential risk due to high volume of water flow in the winter season is also very important. Data (from 1965-1993) from 78 measuring stations located in the 70 distinct watersheds of Cyprus were used. This data volume was divided in the training subset comprising of 60 cases and in the testing subset containing 18 cases. The input parameters are the area of the watershed, the average annual and the average monthly rain-height, the altitude and the slope in the location of the measuring station. Consequently three structural and two dynamic factors are considered. After several and extended training-testing efforts a Modular Artificial Neural Network was determined to be the optimal one. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： artificial neural networks water resources management maximum volume of water-flow back propagation algorithm

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Fault classification of power plants using artificial neural network

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第3期44卷 7665-7680页

作者： Hassan, Muhammad Sabbar Kamal, Khurram Ratlamwala, Tahir Abdul Hussain Natl Univ Sci & Technol Dept Engn Sci Islamabad Pakistan

In order to run power plant operations smoothly, power plant faults need to be detected, located, and classified quickly. For this, artificial neural network approaches are considered significant tool in related application of power system. This research paper focuses on power plant faults detection and classification using intelligent approach of artificial neural network. There is frequent nature of power plant faults, in particular transmission line faults. The key intention of this research is to use ANN approach to detect and classify power plant faults, and in the end to compare their performance and accuracy. In this proposal, the three-phase current and voltage are use as input. backpropagation (BPNN) technique is used in developing the learning algorithm for multi-layer neural network that can accurately and reliably classify faults. The multi-layer neural network such as Levenberg-Marquardt and Bayesian regularization are used for offline training of the data. Findings from this research show that both Levenberg-Marquardt and Bayesian regularization show good accuracy to detect and classify faults in transmission line. However, LM algorithm trained data much faster than BR. LM shows fastest detection of faults with overall mean square error value of 6.13785e-3 at the 91 epoch. Whereas, BR trained network at the 717 epochs with overall mse of 4.67123e-3. It is to be noted that the accuracy of trained network to classify all the fault types is found to be 81.1%, which is higher than previous studies using backpropagation neural network technique. Training and test results show that these neural network-based methods can effectively detect and classify faults and faults types and have adequate performance. Matlab is used in this research because it has toolbox, which has effective features to train required data and achieve effective result.

关键词： back propagation algorithm Levenberg-Marquardt algorithm bayesian regularization artificial neural networks fault classification

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A multilayer perceptron-based medical decision support system for heart disease diagnosis

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EXPERT SYSTEMS WITH APPLICATIONS 2006年第2期30卷 272-281页

作者： Yan, HM Jiang, YT Zheng, J Peng, CL Li, QH Univ Elect Sci & Technol China Life Sci & Technol Inst Chengdu 610054 Peoples R China Univ Nevada Dept Elect & Comp Engn Las Vegas NV 89154 USA Chongqing Univ Bioengn Inst Chongqing 400044 Peoples R China SW Hosp Chongqing 400038 Peoples R China CUNY Queens Coll Dept Comp Sci Flushing NY 11367 USA

The medical diagnosis by nature is a complex and fuzzy cognitive process, and soft computing methods, such as neural networks, have shown great potential to be applied in the development of medical decision support systems (MDSS). In this paper, a multiplayer perceptron-based decision support system is developed to support the diagnosis of heart diseases. The input layer of the system includes 40 input variables, categorized into four groups and then encoded using the proposed coding schemes. The number of nodes in the hidden layer is determined through a cascade learning process. Each of the 5 nodes in the output layer corresponds to one heart disease of interest. In the system, the missing data of a patient are handled using the substituting mean method. Furthermore, an improved back propagation algorithm is used to train the system. A total of 352 medical records collected from the patients suffering from five heart diseases have been used to train and test the system. In particular, three assessment methods, cross validation, holdout and bootstrapping, are applied to assess the generalization of the system. The results show that the proposed MLP-based decision support system can achieve very high diagnosis accuracy (> 90%) and comparably small intervals (< 5%), proving its usefulness in support of clinic decision process of heart diseases. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： medical decision support system multilayer perceptron back propagation algorithm performance of classifier heart disease

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Particle swarm optimization of a neural network model in a machining process

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2014年第3期39卷 533-548页

作者： Garg, Saurabh Patra, Karali Pal, Surjya K. Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Indian Inst Technol Patna Dept Mech Engn Patna 800013 Bihar India Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high speed steel (HSS) drills for drilling on mild steel workpieces, under different sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as the average flank wear in each case. The results show that the PSO trained ANN not only gives better prediction results and reduced computational times compared to the BPNN, it is also a more robust model, being free of getting trapped in local optimum solutions unlike the latter. Besides, it offers the advantages of a straight-forward logic, simple realization and underlying intelligence.

关键词： Particle swarm optimization artificial neural network back propagation algorithm flank wear drilling

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