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Divergence effects for online adaptation of membership functions

INTELLIGENT AUTOMATION AND SOFT COMPUTING 1998年第1期4卷 39-51页

作者： Hofbauer, A Heiss, M Univ Technol Vienna Vienna Austria Siemens AG Villach Austria PSE NLT2 ECANSE A-1040 Vienna Austria

The adaptation of membership functions in a fuzzy system is a nonlinear optimization problem. Thus, the convergence of online learning algorithms is questionable. We demonstrate the convergence problems by analyzing two types of spikes, the narrow basis function spikes and the non-monotonic basis function spikes, which can occur during the online adaptation. Further, we show how these spikes can be avoided by restricting the parameter variations of the widths and the distances of the membership functions. According to these restrictions we have to conclude that in most cases it is better solely to adapt the rule conclusions than to adapt the membership functions.

关键词： fuzzy control learning control basis functions spikes back-propagation algorithm

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Neural network based modelling of environmental variables: A systematic approach

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MATHEMATICAL AND COMPUTER MODELLING 2001年第6-7期33卷 669-682页

作者： Maier, HR Dandy, GC Univ Adelaide Dept Civil & Environm Engn Ctr Appl Modelling Water Engn Adelaide SA 5005 Australia

Feedforward artificial neural networks (ANNs) that are trained with the back-propagation algorithm are a useful tool for modelling environmental systems. They have already been successfully used to model salinity, nutrient concentrations, air pollution, and algal growth. These successes, coupled with their suitability for modelling complex systems, have resulted in an increase in their popularity and their application in an ever increasing number of areas. They are generally treated as black box models that are able to capture underlying relationships when presented with input and output data. In many instances, little consideration is given to potential input data and the internal workings of ANNs. This can result in inferior model performance and an inability to accurately compare the performance of different ANN models. back-propagation networks employ a modelling philosophy that is similar to that of statistical methods in the sense that unknown model parameters (i.e., connection weights) are adjusted in order to obtain the best match between a historical set of model inputs and corresponding outputs. Consequently, the principles that are considered good practice in the development of statistical models should be considered. In this paper, a systematic approach to the development of ANN based forecasting models is presented, which is intended to act as a guide for potential and current users of feedforward ANNs that are trained with the back-propagation algorithm. Issues that need to be considered in the model development phase are discussed and ways of addressing them presented. The major areas covered include data transformation, the determination of appropriate model inputs, the determination of an appropriate network geometry, the optimisation of connection weights, and validation of model performance. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： artificial neural networks back-propagation algorithm forecasting cyanobacteria (blue-green algae) salinity

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Optimisation of the range of an optical fibre pH sensor using feed-forward artificial neural network

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SENSORS AND ACTUATORS B-CHEMICAL 2003年第1-3期90卷 175-181页

作者： Suah, FBM Ahmad, M Taib, MN Univ Kebangsaan Malaysia Sch Chem Sci & Food Technol Fac Sci & Technol Bangi DE Malaysia Univ Teknol MARA Fac Elect Engn Selangor DE Malaysia

A broad range of optical fibre pH sensor based on immobilised bromophenol blue (BPB) immobilised on hydrophobic organic polymers Amberlite XAD 7 is presented in this paper. The reflectance spectra of the immobilised bromophenol blue were measured by using an optical fibre spectrophotometer. A back-propagation (BP) artificial neural network (ANN) model was used to analyse the optode response. The results showed that the use of ANN technique was very effective in broadening the limited dynamic response of the pH sensor (pH 2.00-5.00) to an extensive calibration response (pH 2.00-12.00). A network with 11 neurons in the hidden layer was tremendously accurate in predicting the response of the optical fibre pH sensor with an average error 0.02 pH for measuring unidentified buffer solution. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： optical fibre pH sensor artificial neural network back-propagation algorithm bromophenol blue amberlite XAD 7

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Application of Neural Networks in Early Detection and Diagnosis of Parkinson's Disease

Application of Neural Networks in Early Detection and Diagno...

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International Conference on Cyber and it Service Management (CITSM)

作者： Olanrewaju, Rashidah Funke Sahari, Nur Syarafina Musa, Aibinu A. Hakiem, Nashrul Int Islamic Univ Malaysia Dept Elect & Comp Engn Fac Engn Kuala Lumpur Malaysia Fed Univ Technol Minna Minna Niger State Nigeria UIN Syarif Hidayatullah Jakarta Fac Sci & Technol Dept Informat Engn South Tangerang Indonesia

ISBN: (纸本)9781479979738

Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson's Disease Detection Dataset. The output of the network is classified into healthy or PD by using K-Means Clustering algorithm. The performance of this classifier was evaluated based on the three parameters;sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.

关键词： backpropagation diseases medical computing medical disorders multilayer perceptrons neurophysiology pattern clustering BP algorithm MLFNN Matlab software Oxford Parkinson's disease detection dataset PD detection PD diagnosis PD output Parkinson's disease detection Parkinson's disease diagnosis accuracy parameter back-propagation algorithm chemical dopamine chronic neurological progressive disorder healthy output k-means clustering algorithm multilayer feedforward neural network performance evaluation sensitivity parameter specificity parameter Accuracy Classification algorithms Clustering algorithms Diseases Neural networks Neurons Sensitivity Multilayer feedforward neural network MATLAB back propagation maladjustment Parkinson Disease Clustering algorithms medical computing medical disorders Neural network Multilayer perceptrons Sorting algorithm Neurophysiology Cluster Analysis Performance Evaluation Early Diagnosis

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Grouping Model Application on Artificial Neural Networks for Short-term Load Forecasting

Grouping Model Application on Artificial Neural Networks for...

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7th World Congress on Intelligent Control and Automation

作者： Zhang, Shunhua Lian, Jingjing Zhao, Zhiying Xu, Huijun Liu, Jing Department of Electric and Electronic Engineering Nanchang Institute of Technology Jiangxi CPI Power Engineering CO. LTD Nanchang 330039 China Department of Electric and Electronic Engineering Nanchang Institute of Technology China

ISBN: (纸本)9781424421138

With the development of our country's electricity market, power system load forecasting, especially short-term load forecasting (STLF), has been get more and more important function in the reliable, security, and economical operation of power system. The paper has introduced the principle of artificial neural networks and how we can use it to forecast electric power load. In this paper, a three-layer feed-forward back-propagation (BP) network is applied for short-term load forecasting. In order to improve the accuracy of short-term load forecasting, the data were detached into three groups: workday data, weekend data and festival data which were used to be trained for grouping forecasting models. The proposed networks are trained with 2-year (2002-2003) actual data of Nanchang city and are tested for target years (2004) including workday model, weekend model and festival model. Very reasonable results have been obtained for sample data.

关键词： short-term load forecasting artificial neural networks back-propagation algorithm grouping forecasting models

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Self-tuned PID Control based on backpropagation Neural Networks for Underwater Vehicles

Self-tuned PID Control based on Backpropagation Neural Netwo...

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MTS/IEEE Oceans Conference

作者： Hernandez-Alvarado, R. Garcia-Valdovinos, L. G. Salgado-Jimenez, T. Gomez-Espinosa, Alfonso Fonseca Navarro, F. Ctr Engn & Ind Dev CIDESI Energy Div Santiago De Queretaro Mexico Tecnol Monterrey Campus QueretaroAve Epigmenio Gonzalez 500 Santiago De Queretaro 76130 Queretaro Mexico

ISBN: (纸本)9781509015375

For a long time, PID-like controllers have been successfully used in academic and industrial tasks. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades;most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a need. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool they are fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tuned PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

关键词： Auto-tuning PID Control Neural Network back-propagation algorithm Disturbances

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An Intelligent System for Predicting Thrips Tabaci Linde Pest Population Dynamics Allied To Cotton Crop

An Intelligent System for Predicting Thrips Tabaci Linde Pes...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Patil, Jyothi Govardhan, A. Mytri, V. D. PDA Coll Engn Dept Informat Sci Gulbarga Karnataka India JNT Univ Dept Comp Sci Hyderabad Andhra Pradesh India GND Engn Coll Dept Appl Elect & Commun Bidar India

ISBN: (纸本)9781424445189

The agricultural sector in India is up against a series of problems when it comes to increasing crop productivity. A number of successful researches have been carried out to discover productive agricultural practices to improve crop cultivation but despite their efforts, productivity achieved by most of the farmers has not been in upper-bound level. The prime reason stated globally for crop loss is Insect pests. An efficient pest management technique can be devised if we could predict in advance the occurrences of peak activities of a given pest. Researchers are undertaken to understand the pest population dynamics by employing analytical and other techniques on pest surveillance data sets. In this paper, we present an intelligent system for pest prediction in cotton crop with the aid of the data obtained from College of Agriculture, Raichur, India. We make an effort to understand population dynamics of Thrips tabaci Linde (Thrips) pest on cotton (Gossypium Arboreum) crop using neural networks by analyzing pest surveillance data. The Multi-layer perceptron neural network with back-propagation training algorithm is utilized in the design of the presented intelligent system. The results show that neural network system can be able to give results with a very high degree of accuracy and is best suited to build a prediction system. With the aid of this pest prediction system, the farming communities get more beneficiaries in crop productivity.

关键词： Pest Pest Population Dynamics Pest Surveillance Data Prediction Cotton Crop Thrips Tabaci Linde Intelligent System Neural network Multi-layer Perceptron Neural Network (MLPNN) back-propagation algorithm

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Implementation of Transient Stability Assessment using Artificial Neural Networks

Implementation of Transient Stability Assessment using Artif...

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International Conference on Computer, Electrical and Electronics Engineering (ICCEEE)

作者： Eltigani, Dalia M. Ramadan, Kamal Zakaria, Elfadil Int Univ Africa Dept Elect Engn Khartoum Sudan Univ Khartoum Dept Elect Engn Khartoum Sudan SUST Dept Elect Engn Khartoum Sudan

ISBN: (纸本)9781467362313;9781467362320

This paper aims at verifying the accuracy of Artificial Neural Networks (ANN) in assessing the transient stability of a single machine infinite bus system. The fault critical clearing time obtained through ANN is compared to the results of the conventional equal area criterion method. The multilayer feed-forward artificial neural network concept is applied to the system. The training of the ANN is achieved through the supervised learning;and the back propagation technique is used as a learning method in order to minimize the training error. The training data set is generated using two steps process. First, the equal area criterion is used to determine the critical angle. After that the swing equation is solved using the point-to-point method up to the critical angle to determine the critical clearing time. Then the stability of the system is verified. As a result we find that the critical clearing time is predicted with slightly less accuracy using ANN compared to the conventional methods for the same input data sets unless the ANN is well trained.

关键词： Multilayer Feed-forward network back-propagation algorithm critical clearing time point-to-point method

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Passive Fault Tolerant Control System Using Feed-forward Neural Network for Two-Tank Interacting Conical Level Control System Against Partial Actuator Failures and Disturbances 18th

Passive Fault Tolerant Control System Using Feed-forward Neu...

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18th IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing (MMM)

作者： Patel, Himanshukumar R. Shah, Vipul A. Dharmsinh Desai Univ Nadiad 387001 Gujarat India

A novel approach for passive fault-tolerant control (PFTC) system design against actuator faults is proposed. The scheme is based on Feed-forward Neural Network (FFNN) plus conventional PI controller, during the fault occurred into system FFNN will give additional control output to the system according to fault magnitude. The FFNN will trained using back-propagation algorithm. To eliminate the steady-state tracking error, the PI controller is also incorporated. The following fault type and input signals are considered: abrupt, step, sine, and trapezoidal trajectory inputs. The effectiveness and the superiority of the proposed approach are demonstrated using Two-Tank Interacting Conical Level Control System (TTICLCS) example. The simulation performed in MATLAB Simulink platform, also different integral errors like IAE, ISE and IATE are presented to validate the proposed approach. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Actuator fault back-propagation algorithm conical tank disturbances Feed-forward Neural Network fault-tolerant control PI control

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A Kind Improved Model of Information Fusion

A Kind Improved Model of Information Fusion

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International Seminar on Business and Information Management (ISBIM 2008)

作者： Huang, Nantian Lin, Lin Jilin Inst Chem Technol Coll Informat & Control Engn Jilin Peoples R China

ISBN: (纸本)9780769535609

Information fusion is an important research field, one major theory and technology is neural networks especially back-propagation (BP) neural network. Meanwhile BP neural network has been applied in many fields. But traditional BP neural network has some faults, such as bad convergence rate and low learning rate aiming at huge date sets, poor generalization, poor ability of error weight update and batch learning. Aiming at these faults, some improved methods are proposed to solve these problems, one of methods is BP neural network based on Kalman Filter which can solve before-mentioned faults partly. But present methods of BP neural network based on Kalman Filter can not do batch processing and study multi-sample conditions. Improved BP neural network based on Kalman Filter is proposed depending on present BP neural network based on Kalman Filter. The idea of new method includes two steps, firstly we obtain the update of estimation weight, secondly we use the obtained results to mend the Kalman Gain for new update of time and measurement, at the some time the new algorithm can adopt batch processing to learning neural network. Experiments show the new algorithm can solve high-dimensional, large computation problem, keeping robustness and improving the learning efficiency.

关键词： information fusion (IF) neural network (NN) back-propagation algorithm Kalman Filter (KF) batch processing

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