Back-propagation Network Algorithm Research Articles

ANN based cutting tool condition monitoring system for high-speed turning operations

Supervision or condition monitoring of the cutting tool wear is a complex phenomenon in any machining process. This paper describes an in-depth study on the development of a cutting tool condition monitoring (TCM) system for high speed turning operations. An attempt has been made to create artificial flank wear using electrical discharge machining (EDM) process. Using this (artificial worn out) cutting tool, experiments were carried out for various machining conditions. Vibration and strain data from the cutting process are recorded using two accelerometers and a strain gauge bridge. Time domain and power spectral analyses are carried out and an artificial neural networks (ANN) back propagation algorithm is used to classify and estimate the wear parameters.

AN INTEGRATED APPROACH FOR THE PREDICTION OF WATER QUALITY INDEX BASED ON LAND USE ATTRIBUTES USING DATA GENERATION METHOD AND BACK PROPAGATION NETWORK ALGORITHM

Malaysian experts have warned that several major cities, including the capital Kuala Lumpur, could face serious water shortages due to over-pollution of the country's rivers by problems brought about by over-development. As 97 per cent of Malaysia's water supply is sourced from surface water, the main aim of this study was to identify a relationship between water quality and land use attributes. The study which was conducted on Gombak River and its watershed in Malaysia, introduced data generation method for the prediction and forecast of LU/LC data within the watershed. The method used exponential model equation, Lagrange model equation third & fourth degree polynomial fit; saturation growth-rate model in order to generate the required data: and artificial neural network's back propagation network algorithm. The study also introduces the LA-WQI model. This model was developed by associating the appropriate loading factors to a set of sub-indices. The findings revealed that as the activities increased throughout the watershed, the values of WQI quality decreased accordingly. The accuracy of prediction of the proposed LA-WQI ranged from 94.3% to 99.3% between Actual DOE-WQI and LA-WQI for station 18 in Gombak River. The results of predicted WQI obtained using LA-WQI, showed a continuous decrease of water quality. Despite the high accuracy attained by the application of LA-WQI model on Gombak River; it has not yet been tested on other rivers. It is recommended that future studies should be able to further test the current model on a regional scale.

AN INTEGRATED APPROACH FOR THE PREDICTION OF WATER QUALITY INDEX BASED ON LAND USE ATTRIBUTES USING DATA GENERATION METHOD AND BACK PROPAGATION NETWORK ALGORITHM

Malaysian experts have warned that several major cities, including the capital Kuala Lumpur, could face serious water shortages due to over-pollution of the country's rivers by problems brought about by over-development. As 97 per cent of Malaysia's water supply is sourced from surface water, the main aim of this study was to identify a relationship between water quality and land use attributes. The study which was conducted on Gombak River and its watershed in Malaysia, introduced data generation method for the prediction and forecast of LU/LC data within the watershed. The method used exponential model equation, Lagrange model equation third & fourth degree polynomial fit; saturation growth-rate model in order to generate the required data: and artificial neural network's back propagation network algorithm. The study also introduces the LA-WQI model. This model was developed by associating the appropriate loading factors to a set of sub-indices. The findings revealed that as the activities increased throughout the watershed, the values of WQI quality decreased accordingly. The accuracy of prediction of the proposed LA-WQI ranged from 94.3% to 99.3% between Actual DOE-WQI and LA-WQI for station 18 in Gombak River. The results of predicted WQI obtained using LA-WQI, showed a continuous decrease of water quality. Despite the high accuracy attained by the application of LA-WQI model on Gombak River; it has not yet been tested on other rivers. It is recommended that future studies should be able to further test the current model on a regional scale.

Using back propagation network algorithms with joint purchase techniques to make profits in a leisure farming district

After five years since Taiwan joining in W.T.O. in 2000, foreign agricultural products are allowed to import freely into the country. Facing the impact, how to reduce the production cost of agricultural products will be an important topic. In a time of micro-profit, the production cost has often become an important factor that may affect the operation of farms in leisure farming districts. It is particularly obvious to the production cost of agricultural products. Joint purchase is one of effective and feasible way to reduce production cost. Joint purchase may achieve the purpose of using quantity to restrain and reduce prices. However, in implementing joint purchase in the leisure farming districts could often face difficulties in finding joint purchase cycle and purchase volume. Therefore, with a hope to reduce production cost and enhances competitiveness of leisure farms and creates the second period of prosperity for agriculture in Taiwan. This paper tries to find a feasible way in searching for joint purchase cycle and purchase volume in implementing joint purchase in the leisure farming district by using back propagation network algorithms and combining the use of maximum profit function.

Determination of optimal hierarchical fuzzy controller parameters according to loading condition with ANN

This paper represents an artificial neural network (ANN) backpropagation algorithm is used to choose best coefficients of hierarchical fuzzy power system stabilizer (HFPSS). PSS is used for stability enhancement of a single machine infinite bus (SMIB) power system. ANN algorithm is used to predict load condition of the power system. And according to the predicted load condition ANN determinates choosing optimal parameters of the hierarchical fuzzy controller (HFC) to achieve better performance. Simulation results are compared with conventional PSS (CPSS) to show the effectiveness of the proposed controller. Also quantitative criterias of measuring performance is computed for 16 loading conditions.

The detection of esophagitis by using Back Propagation Network algorithm

The results of this study suggest the use of a Back Propagation Network (BPN) algorithm for the detection of esophageal erosions or abnormalities—which are the important signs of esophagitis—in the analysis of the color and textural aspects of clinical images obtained by endoscopy. The authors have investigated the optimization of the learning condition by the number of neurons in the hidden layer within the structure of the neural network. By optimizing learning parameters, we learned and have validated esophageal erosion images and/or ulcers functioning as the critical diagnostic criteria for esophagitis and associated abnormalities. Validation was established by using twenty clinical images. The success rates for detection of esophagitis during calibration and during validation were 97.91% and 96.83%, respectively.

Parallel implementation of back-propagation algorithm in networks of workstations

This work presents an efficient mapping scheme for the multilayer perceptron (MLP) network trained using back-propagation (BP) algorithm on network of workstations (NOWs). Hybrid partitioning (HP) scheme is used to partition the network and each partition is mapped on to processors in NOWs. We derive the processing time and memory space required to implement the parallel BP algorithm in NOWs. The performance parameters like speed-up and space reduction factor are evaluated for the HP scheme and it is compared with earlier work involving vertical partitioning (VP) scheme for mapping the MLP on NOWs. The performance of the HP scheme is evaluated by solving optical character recognition (OCR) problem in a network of ALPHA machines. The analytical and experimental performance shows that the proposed parallel algorithm has better speed-up, less communication time, and better space reduction factor than the earlier algorithm. This work also presents a simple and efficient static mapping scheme on heterogeneous system. Using divisible load scheduling theory, a closed-form expression for number of neurons assigned to each processor in the NOW is obtained. Analytical and experimental results for static mapping problem on NOWs are also presented.

A handwritten numeral character classification using tolerant rough set

Proposes a data classification method based on the tolerant rough set that extends the existing equivalent rough set. A similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity threshold value is very important for accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that: 1) some tolerant objects are required to be included in the same class as many as possible; and 2) some objects in the same class are required to be tolerant as much as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grouped into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method such that all data are classified by using the lower approximation at the first stage and then the nonclassified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification problem and compare its classification performance and learning time with those of the feedforward neural network's backpropagation algorithm.

Development of a back propagation network for one-step transient DNBR calculations

One of the key safety parameters during the transient of pressurized water reactor is the departure from nucleate boiling ratio (DNBR). In the transient analysis caused by the anticipated operational occurrences or accidents, the DNBR is predicted by three steps: firstly, a system transient analysis code, secondly, a heat flux calculation code and finally a detailed DNBR calculation code should be used. This tandem procedure is time consuming and very cumbersome. In this paper, the back propagation network (BPN) algorithm is incorporated into the system transient analysis code for the one-step transient DNBR calculation, thus, to eliminate the tandem procedure and to increase calculation speed. The BPN is trained with the DNBR training data selected by latin hypercube sampling technique. After the completion of training, performance is evaluated. Results show that the system transient analysis code with a multi-layer BPN algorithm can quickly calculate the transient DNBR with the reasonable accuracy even in case of axial flux shape changes.

Thermal power prediction of nuclear power plant using neural network and parity space model

A power prediction system was developed using an artificial neural network paradigm that was combined with a parity space signal validation technique. The parity space signal validation algorithm for input preprocessing and a backpropagation network algorithm for network learning are used for the power prediction system. Case studies were performed with emphasis on the applicability of the network in a steady-state high-power level. The studies reveal that these algorithms can precisely predict the thermal power in a nuclear power plant. They also show that the error signals resulting from instrumentation problems can be properly treated even when the signals comprising various patterns are noisy or incomplete. >