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Development of a hybrid neural network system for prediction of process parameters in injection moulding

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2001年第1-3期118卷 110-116页

作者： Yarlagadda, PKDV Khong, CAT Queensland Univ Technol Sch Mech Mfg & Med Engn Brisbane Qld Australia

In this paper, the attempts made by the authors to develop an artificial neural network system for prediction of injection moulding process parameters is presented. In this work, attempts have been made to determine the process parameters that could affect injection moulding process based on governing equations of the filling process. Focus is then directed to parameters that require the use of trial and error methods or other complex software to determine the process parameters. The two parameters that are predicted from the developed network are injection time and injection pressure. In this work, the training data are generated by simulation using C-MOLD flow simulation software. A total of 114 data were collected out of which 94 were used to train the network using MATLAB and the remaining 20 for testing the network. Two algorithms are used during the training phase, namely the error-back-propagation algorithm and the Levenberg-Marquardt approximation algorithm. Results showed that the latter algorithm is more suitable for this a plication since the Leverberg's algorithm converged rapidly with lesser training cycles when compared to the error-back-propagation algorithm. The accuracy of the developed network has been tested by predicting the injection pressure and injection time for few engineering components and found that the overall error is 0.93% with a deviation of 3.93%. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： artificial intelligence hybrid neural networks injection moulding back propagation algorithm

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Estimating Software Effort Using Neural Network: An Experimental Investigation 1

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2nd International Conference on Computational Intelligence in Pattern Recognition (CIPR)

作者： Kumar, P. Suresh Behera, H. S. Veer Surendra Sai Univ Technol Dept IT Burla 768018 India

ISBN: (数字)9789811524493

ISBN: (纸本)9789811524493;9789811524486

Software companies develop various softwares at the same time. It is very critical task that is to be managed by project managers. Completion of a project is purely dependent on various parameters such as time, cost and staff. By these parameters, project planning will be done by the project managers. Software effort estimation is a fundamental and emerging aspect for software companies in developing a software. If we estimate the effort properly, it will control the project cost as well as time. This paper is presented by comparing various models such as KNN, SVM, NN, RF and back propagation algorithm using feed forward neural network by using Orange data mining tool. The proposed models are evaluated using COCOMO'81 dataset having 63 projects and Desharnais dataset having 81 projects. Estimation results are evident that the back propagation-based approach is a suitable model as compared to the remaining considered approach.

关键词： Software effort estimation back propagation algorithm K-nearest neighbor regression Support vector machine Neural network Random forest

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An APSO Optimized BP Neural Network

An APSO Optimized BP Neural Network

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IEEE International Conference on Electronics, Communications and Control (ICECC)

作者： Mo Lihong Huaiyin Inst Technol Fac Elect & Elect Engn Huaian Peoples R China

ISBN: (纸本)9781457703218

In this paper, the limitations of conventional BP algorithm was analyzed, and to fasten the learning velocity of neural network and enhance its generalization capability, the APSO (adaptive particle swarm optimization) algorithm was introduced into BP network for the optimization of its weights and thresholds. To overcome its 'early maturity', the variance operation was made on particles owing bigger fitness values according to certain probability, and the inertia weights were dynamic changed during the iteration process. The algorithm was simulated on a nonlinear function regression problem, which shows that the improved PSO-BP algorithm has faster learning velocity and better generalization ability than conventional BP network. The simulation results prove that the APSO-BP algorithm can get over the limitations of the conventional BP network and is superior to it.

关键词： back propagation algorithm particle swarm optimization generalization capability Simulation

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Detection of Missing Power Meter Readings using Artificial Neural Networks 26

Detection of Missing Power Meter Readings using Artificial N...

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XXVI International Conference on Information, Communication and Automation Technologies (ICAT)

作者： Jahic, Admir Konjic, Tatjana Hivziefendic, Jasna Univ Tuzla Fac Elect Engn Dept Power Syst & Networks Tuzla Bosnia & Herceg Int Burch Univ Fac Engn & Nat Sci Elect & Elect Engn Sarajevo Bosnia & Herceg

ISBN: (纸本)9781538633373

In a power distribution network, network topology information is essential for an efficient operation of the network. This information is not accurately available, due to uninformed changes that happen from time to time, or uncertain meter readings. Reliable prediction of system status is a highly demanded functionality of smart energy systems, which can enable users or human operators to react quickly to potential future system changes. This paper presents potential of artificial neural networks to determine missing power meter readings in medium voltage (MV) networks where the number of on-line measurements is limited, and state estimation relies heavily on estimates of power injections. The applicability of the approach is demonstrated through simulation using supervisory control and data acquisition and smart meter measurements recorded from an actual MV distribution network. Results are showing that artificial neural networks can have 100% measurements detection accuracy.

关键词： forecast aided state estimation pseudo measurements power meter readings artificial neural networks back propagation algorithm

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Over-The-Air Calibration of A Phase Shifter Network

Over-The-Air Calibration of A Phase Shifter Network

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Zhang, Wei Jiang, Yi Fudan Univ Dept Commun Sci & Engn Shanghai Peoples R China

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

Phase shifter networks (PSN) are now widely used in multi-input multi-output (MIMO) systems for its low cost and analog signal processing capability. In practice, the phase shifters may be subject to phase deviations, which needs to be properly estimated and calibrated. This paper proposes a novel over-the-air (OTA) approach to estimate the deviations of the phase shifters at each gear. We formulate the PSN calibration model by the so-termed quasi-neural network (quasi-NN). In training the quasi-NN using the back propagation (BP) algorithm, the phase deviations are automatically estimated. The simulation results verify the effectiveness of the proposed algorithm by showing that the root mean square errors (RMSEs) of the phase estimates are close to the Cramer Rao Bounds (CRBs).

关键词： MIMO quasi-neural network phased array calibration back propagation algorithm

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Handwritten Character Recognition-An Analysis

Handwritten Character Recognition-An Analysis

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International Conference on Advancement in Energy, Drive and Control (ICAEDC)

作者： Tiwari, Usha Jain, Monika Mehfuz, Shabana Jamia Millia Islamia New Delhi India ITS Engn Coll Dept Elect & Elect Engn Greater Noida India

ISBN: (纸本)9789811306655;9789811306648

Handwriting recognition has always been an active and challenging research area with various applications in daily life and also in area of pattern recognition. Some of the applications are reading tool for blind people, reading handwritten bank cheques, by converting it into properly structured text which can be easily detected by the designed algorithms. This paper gives brief analysis of various handwritten recognition techniques, such as Optical Character Recognition (OCR), Artificial Neural Network (ANN), Intelligent Character Recognition (ICR), and Intelligent Word Recognition (IWR). Accuracy rates of all these methods were compared, and the comparative analysis demonstrates that OCR method is the best among them for the recognition of English handwritten characters. Merits and demerits are also discussed for the methods of recognition. Basic steps involved in the handwritten recognition process are also briefed.

关键词： Optical Character Recognition (OCR) Neural network topologies back propagation algorithm Feature extraction process

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A Novel Approach of Input Variable Selection for ANN Based Load Forecasting

A Novel Approach of Input Variable Selection for ANN Based L...

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Joint International Conference on Power Systems Technology (POWERCON)/IEEE Power India Conference

作者： Shrivastava, Vivek Misra, R. B. Indian Inst Technol Ctr Reliabil Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781424417636

The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using Artificial Neural Networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. A case study is performed using the proposed method of peak load data of the Grid Corporation of Orrissa (GRIDCO), India which maintain high quality, reliable, historical data.

关键词： back propagation algorithm Load Forecasting Multi-layered Neural Model Neural Network

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Feature Extraction and Recognition of Bengali Word Using Gabor Filter and Artificial Neural Network

Feature Extraction and Recognition of Bengali Word Using Gab...

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International Conference on Infirmation Systems for Indian Languages (ICISIL 2011)

作者： Nandy (Pal), Mahua Majumdar, Sumit MCKV Inst Engn Dept CSE Howrah W Bengal India

ISBN: (纸本)9783642194023

Character recognition is an emerging area of research in the field of image processing and pattern recognition. The objective here is to generate a Bengali dictionary and develop an artificial neural network based technique for matching an input word with the dictionary word. The technique uses the features of the words as a whole rather than the features of each character. For feature extraction purpose, we have used 2D Gabor filter. For the dictionary words, it shows 93.67% accuracy in matching and for non-dictionary words, it shows 83% accuracy in non-matching. The overall accuracy of the system becomes 91%.

关键词： Gabor filter iLeap binarization normalization orientation matrix back propagation algorithm

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A Novel approach to Predict Diabetes Mellitus using Modified Extreme Learning Machine

A Novel approach to Predict Diabetes Mellitus using Modified...

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International Conference on Electronics and Communication Systems (ICECS)

作者： Priyadarshini, Rojalina Dash, Nilamadhab Mishra, Rachita CV Raman Coll Engn Bhubaneswar Orissa India

ISBN: (纸本)9781479966387

Data Classification and predictions are one of the prime tasks in Data mining. They continue to play a vital role in the area of computer science and data processing field. Clustering and classifications in Data Mining are used in various domains to give meaning to the available data and give some useful prediction results which can be applied to some of the crucial problem areas of the real world. Diabetes mellitus otherwise known as a slow poison by the medical experts is a major, alarming and gradually becoming a global problem. This paper experimented and used the concept of modified extreme learning machine to identify the patients of being diabetic or nondiabetic basing on some previously given data which in turn helps the medical people to identify whether someone is affected by diabetes or not. It also describes and compares the application of two popular machine learning methods: back propagation neural network and modified Extreme learning machine which are used as binary classifiers to address the diabetes prediction problem. These two approaches are applied on same type of multi class classification datasets and the work tries to generate some comparative inferences from training and testing results. The datasets which are used in our work is taken from UCI learning repository

关键词： Extreme learning machine Diabetes mellitus Classification back propagation algorithm

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A Learning System for Detecting Transformer Internal Faults

A Learning System for Detecting Transformer Internal Faults

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International Conference on Informatics, Electronics and Vision (ICIEV)

作者： Saleh, Abdullah Md. Hossain, Md. Zakir Rabin, Md. Jubayer Alam Kabir, A. N. M. Enamul Khan, Md. Fazle Elahi Shahjahan, Md. Khulna Univ Engn & Technol Khulna 9203 Bangladesh

ISBN: (纸本)9781479904006;9781479903979

Miniature transformer is one of the most important components of electronic devices. A serious failure of such kind of transformer may cause loss of time and money. This paper presents a learning system to recognize internal fault of such kind of transformer. The different kinds of faults are made to occur intentionally and data are collected at various conditions. The faults include turn to turn, winding to ground, and dielectric faults. The data are then processed and entered in the learning algorithms to recognize the type of fault. We devise a learning system to recognize the various types of faults. Several versions of learning algorithms such as standard back propagation, Levenberg-Marquardt, Bayesian regulation, Resilient back propagation, Gradient descent, One-step secant, Elman recurrent network are used. The result of Levenberg-Marquardt algorithm was found to be faster than that of other algorithms. Therefore it is suitable for real time fault detection.

关键词： Miniature transformer internal fault neural network (NN) back propagation algorithm fault detection

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