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Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns

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JOURNAL OF INTELLIGENT MANUFACTURING 2010年第6期21卷 717-730页

作者： Purushothaman, Srinivasan Multimedia Univ Fac Engn & Technol Melaka Malaysia

The condition of the tool in a turning operation is monitored by using artificial neural network (ANN). The recursive Kalman filter algorithm is used for weight updation of the ANN. To monitor the status of the tool, tool wear patterns are collected. The patterns are transformed from n-dimensional feature space to a lower dimensional space (two dimensions). This is done by using two discriminant vectors phi(1) and phi(2). These discriminant vectors are found by optimal discriminant plane method. Thirty patterns are used for training the ANN. A comparison between the classification performances of the ANN trained without reducing the dimensions of the input patterns and with reduced dimensions of the input patterns is done. The ANN trained with transformed tool wear patterns gives better results in terms of improved classification performance in less iteration, when compared with the results of the ANN trained without transforming the dimensions of the input patterns to a lower dimension.

关键词： back-propagation algorithm Extended Kalman filter Optimal discriminant plane n-Dimensional vector 2 Dimensional vector Transformation

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Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第2期42卷 885-893页

作者： Chandwani, Vinay Agrawal, Vinay Nagar, Ravindra Malaviya Natl Inst Technol Jaipur Dept Civil Engn Jaipur Rajasthan India

The paper explores the usefulness of hybridizing two distinct nature inspired computational intelligence techniques viz., Artificial Neural Networks (ANN) and Genetic algorithms (GA) for modeling slump of Ready Mix Concrete (RMC) based on its design mix constituents viz., cement, fly ash, sand, coarse aggregates, admixture and water-binder ratio. The methodology utilizes the universal function approximation ability of ANN for imbibing the subtle relationships between the input and output variables and the stochastic search ability of GA for evolving the initial optimal weights and biases of the ANN to minimize the probability of neural network getting trapped at local minima and slowly converging to global optimum. The performance of hybrid model (ANN-GA) was compared with commonly used back-propagation neural network (BPNN) using six different statistical parameters. The study showed that by hybridizing ANN with GA, the convergence speed of ANN and its accuracy of prediction can be improved. The trained hybrid model can be used for predicting slump of concrete for a given concrete design mix in quick time without performing multiple trials with different design mix proportions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Artificial Neural Networks Genetic algorithms back-propagation algorithm Lavenberg Marquardt training algorithm Concrete slump Ready mix concrete

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Mapping of neural network models onto massively parallel hierarchical computer systems

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JOURNAL OF SYSTEMS ARCHITECTURE 1999年第11期45卷 919-929页

作者： Mahapatra, S Mahapatra, RN Chatterji, BN Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

This paper investigates the proposed implementation of neural networks on massively parallel hierarchical computer systems with hypernet topology. The proposed mapping scheme takes advantage of the inherent structure of hypernets to process multiple copies of the neural network in the different subnets, each executing a portion of the training set, and finally combines the weight changes in the subnets to adjust the synaptic weights in all the copies. An expression is derived to estimate the time for all-to-all broadcasting, the principal mode of communication in the parallel implemention of neural networks. This is later used to estimate the time required for executing various execution phases in the neural network algorithm, and thus, to estimate the speedup performance of the proposed implementation. (C) 1999 Published by Elsevier Science B.V. All rights reserved.

关键词： multilayer perceptron training set parallelism hypernet subnets back-propagation algorithm speedup performance

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Controller Application of a Multi-Layer Quantum Neural Network with Qubit Neurons

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2012年第4期6卷 526-540页

作者： Takahashi, Kazuhiko Kurokawa, Motoki Hashimoto, Masafumi Doshisha Univ Dept Informat Syst Design Kyotanabe Kyoto 6100321 Japan Doshisha Univ Grad Sch Kyotanabe Kyoto 6100321 Japan Doshisha Univ Dept Intelligent Informat Engn & Sci Kyotanabe Kyoto 6100321 Japan

This paper investigates a quantum neural network and discusses its application in control systems. A learning-type neural network-based controller that uses a multi-layer quantum neural network having qubit neurons as its information processing unit is proposed. Three learning algorithms;a back-propagation algorithm, a conjugate gradient algorithm and a real-coded genetic algorithm, are investigated to supervise the training of the multi-layer quantum neural network. To evaluate the learning performance and the capability of the quantum neural network-based controller, we conducted computational experiments for controlling a nonlinear discrete-time plant and a nonholonomic system - in this study a two-wheeled robot. The results of computational experiments confirm both the feasibility and the effectiveness of the quantum neural network-based controller and that the real-coded genetic algorithm is suitable for the learning method of the quantum neural network-based controller.

关键词： Quantum Neural Network Qubit Neuron back-propagation algorithm Conjugate Gradient algorithm Real-Coded Genetic algorithm Direct Controller Nonholonomic Robot

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Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information

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JOURNAL OF APPLIED GEOPHYSICS 2015年 117卷 118-125页

作者： Silva, Adrielle A. Lima Neto, Irineu A. Missagia, Roseane M. Ceia, Marco A. Carrasquilla, Abel G. Archilha, Nathaly L. North Fluminense State Univ LENEP UENF BR-27925310 Macae RJ Brazil

Petrographic class identification is of great importance to petroleum reservoir characterization and wellbore economic viability analysis, and is usually performed using core or geophysical log analysis. The coring process is costly, and well log analysis requires highly specific knowledge. Thus, great interest has arisen in new methods for predicting the lithological and textural properties of a wide area from a small number of samples. The artificial neural network (ANN) is a computational method based on human brain function and is efficient in recognizing previously trained patterns. This paper demonstrates petrographic classification of carbonate-siliciclastic rocks using a back-propagation neural network algorithm supported by elastic, mineralogical, and textural information from a well data set located in the South Provence Basin, in the southwest of France. The accuracy of the testing suggests that an ANN application offers an auxiliary tool for petrographic classification based on well data, specifically for prediction intervals in wells that have not been sampled or wells adjacent to sampled wells. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Artificial neural network back-propagation algorithm Recognizing patterns Petrographic classification Carbonate-siliciclastic rocks

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Neural network for wave forecasting among multi-stations

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OCEAN ENGINEERING 2002年第13期29卷 1683-1695页

作者： Tsai, CP Lin, C Shen, JN Natl Chung Hsing Univ Dept Civil Engn Taichung 402 Taiwan

Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： neural network back-propagation algorithm wave forecasting data supplement

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Neural-network-based space-vector pulse-width modulation for capacitor voltage balancing of threephase three-level improved power quality converter

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IET POWER ELECTRONICS 2014年第4期7卷 973-983页

作者： Langer, Nitin Bhat, Abdul Hamid Agarwal, Pramod Natl Inst Technol Srinagar Dept Elect Engn Kashmir India Indian Inst Technol Roorkee Dept Elect Engn Uttrakhand India

In this study, an artificial neural-network (ANN)-based space-vector pulse-width modulation (SVPWM) for capacitor voltage balancing of a three-phase three-level neutral-point clamped converter with improved power quality is presented. The neural-network-based controller offers the advantage of very fast implementation of the SVPWM algorithm. This makes it possible to use an application specific integrated circuit chip in place of a digital signal processor. The proposed scheme employs single layer feed-forward neural-networks at different stages along with a control algorithm using modified reference vector for capacitor voltage balancing of an improved power quality three-phase neutral-point clamped converter. In other words, the neural-network receives three-phase voltages and currents as input and generates symmetrical pulse-width modulation waves for three phases of the converter. A simulated digital signal processor (DSP)-based modulator generates the data which are used to train the network by a back-propagation algorithm in the MATLAB Neural Network Toolbox. The simulation of converter with ANN-based space-vector modulator shows excellent performance when compared with that of conventional DSP-based modulator.

关键词： application specific integrated circuits backpropagation feedforward neural nets neurocontrollers power capacitors power supply quality PWM power convertors neural-network-based space-vector pulse-width modulation capacitor voltage balancing three-phase three-level improved power quality converter three-phase three-level neutral-point clamped converter neural-network-based controller SVPWM algorithm application specific integrated circuit chip digital signal processor single layer feed-forward neural-networks modified reference vector symmetrical pulse-width modulation waves DSP-based modulator back-propagation algorithm Matlab neural network toolbox ANN-based space-vector modulator

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Prediction of accumulated temperature in vegetation period using artificial neural network

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MATHEMATICAL AND COMPUTER MODELLING 2010年第11-12期51卷 1453-1460页

作者： Mi, Chunqiao Yang, Jianyu Li, Shaoming Zhang, Xiaodong Zhu, Dehai China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

In this paper, the theory of artificial neural network with back-propagation algorithm (BPN) is presented, and the BPN model is used to predict the accumulated temperature for Northeast China, North China, and the Huang-Huai-Hai Plain. A total of 235 records collected from 235 meteorology stations were fed into the BPN model for training and testing. The latitude, longitude and elevation of each station were used as input variables of BPN, and the accumulated temperature as output variable. Other key network parameters, such as learning rate, momentum, the number of hidden nodes, and the learning iterations, were optimized using a trial and error approach. The optimized BPN model was compared with the multiple linear regression (MLR) model. In summary, BPN model was generally more accurate than MLR model. This infers that artificial neural network models are more applicable than regression models when predicting accumulated temperature. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Artificial neural network back-propagation algorithm Accumulated temperature prediction

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An improved neural network approach to the determination of aquifer parameters

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JOURNAL OF HYDROLOGY 2006年第1-4期316卷 281-289页

作者： Lin, GF Chen, GR Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan

In this paper, an artificial neural network (ANN) approach to the determination of aquifer parameters is developed. The approach is based on the combination of an ANN and the Theis solution. The proposed ANN approach has advantages over the existing ANN approach. It avoids inappropriate setting of a trained range. It also determines the aquifer parameters more accurately and needs less required training time. Testing the existing and the proposed ANN approaches by 1000 sets of synthetic data also demonstrates these advantages. As to the comparison between the proposed ANN approach and the type-curve graphical method, an application to actual time-drawdown data shows that the proposed ANN approach determines the aquifer parameters more precisely. The proposed ANN approach is recommended as an alternative to the type-curve graphical method and the existing ANN approach. (c) 2005 Elsevier B.V. All rights reserved.

关键词： aquifer parameters artificial neural network back-propagation algorithm aquifer test

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Towards an improved Adaboost algorithmic method for computational financial analysis

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2019年 134卷 219-232页

作者： Chang, Victor Li, Taiyu Zeng, Zhiyang Teesside Univ Sch Comp & Digital Technol Middlesbrough Cleveland England Xian Jiaotong Liverpool Univ Int Business Sch Suzhou Suzhou Peoples R China

Machine learning can process data intelligently, perform learning tasks and predict possible outputs in time series. This paper presents the use of our proposed machine learning algorithm;an Adaptive Boosting (Adaboost) algorithm, in analyzing and forecasting financial nonstationary data, and demonstrating its feasibility in financial trading. The data of future contracts are used in our analysis. The future used to test the Adaboost algorithm is a contract chosen to study future IF1711, which is combined by "HS300 index and Rb", the deformed steel bar future in Chinese stock market. The predicted data is compared with real world data to calculate accuracy and efficiency. The Adaboost algorithm is combined with an Average True Range-Relative Strength Index (ATR-RSI) strategy, so that it can be applied in automatic trading and therefore demonstrate its practical application We develop three additional algorithms to enable optimization, large sale simulations and comparing both the predicted and actual pricing values. We performed experiments and large scale simulations to justify our work. We have tested the accuracy and validity of our approach to improve its quality. In summary, our analysis and results show that our improved Adaboost algorithms may have useful and practical implications in nonstationary data analysis. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Nonstationary data Nonstationary time series Machine learning back-propagation algorithm Adaptive boosting algorithm ATR-RSI strategy

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