... applicationsin intelligent manufacturing. Neural Network Applications in Intelligent ManufacturingSystem Modelingand DesignProcess Modeling,Planning andSchedulingProcess Monitoringand ControlQuality ... backpropagationlearning rule of neural networks, Computers and Industrial Engineering, 20, 425, 1991. 40. Jamal, A. M. M., Neural networks and cellular manufacturing: The benefits of applying a neural network ... theuse of neural networks is still constrained to simulations on sequential computing machines. Traininga large network using a sequential machine can be time-consuming. Fortunately, training usually...
... The binary floating point file format is expedient when you have a large amount of data. The data is saved in aseparate file as a sequence of floating point numbers in binary format, using 4 ... backprop training are optional. You may use them for validationand testing of your network, for input data normalization, and error limits during training process.>ann1dn t network. nn data1_file ... layer:ANNetwork::ANNetwork(const wchar_t *fname);ANNetwork::ANNetwork(int layers_number, int *neurons_per_layer);int nerons_per_layer[4] = {128, 64, 32, 10};ANNetwork *ann = new ANNetwork(4,...
... neural networks; the second volume contains artifi cial neuralnetworkapplicationsin industrial and control engineering. This second volume begins with a part of artifi cial neuralnetworkapplications ... application of ANN in textiles and clothing industries will be addressed in last section. ArtificialNeural Networks - Industrial and Control EngineeringApplications 6 neural net produced ... pilling, finger marks, and others. free online editions of InTech Books and Journals can be found atwww.intechopen.com ArtificialNeural Networks - Industrial and Control Engineering Applications Edited...
... as the input units of the neural network. They used a back propagation neuralnetwork by eight defect samples for off line training. The initial learning rate was 0.1; keeping reducing to 0.01 ... beyond 3 mm, breaking strength, breaking ArtificialNeural Networks - Industrial and Control EngineeringApplications 24 Study Area No Title Author Journal Year Vol(No),pp. Findings Limitations ... results were obtained from the generalized feed forward neuralnetwork algorithms. He examined the predictive power by ArtificialNeural Networks - Industrial and Control Engineering Applications...
... percentage ArtificialNeural Networks - Industrial and Control EngineeringApplications 88 Sao, K.P. & Jain, A. K. (1995). Mercerization and crimp formation in jute. Indian Journal ... of Intelligent Methods for Evaluating the Apparent Quality of Knitted Fabrics. EngineeringApplications of Artificial Intelligence, Vol.23, pp. 217-221, ISSN 0952-1976 ArtificialNeural Networks ... percentage compression by ANN model ArtificialNeural Networks - Industrial and Control EngineeringApplications 74 Tenacity in the machine direction Tenacity in the transverse direction Predicted...
... training2ndANN training3rdANN training4thANN training5thANN trainingRandomly initialized weights & biasesWeights & biases from the 1sttraining1st training ... description of artificialneuralnetwork configuration ArtificialNeural Networks - Industrial and Control EngineeringApplications 120 , where xi is the input of node j of the input layer, ... step training; (b) – in the beginning of the 2nd step training; (c) – at the end of the training. On each screenshot: the menu on the left defines training parameters; the graph in middle-top...
... 14.14GPa which is corresponding to the sintering temperature of 1420°C and the holding time of 80min, while ArtificialNeural Networks - Industrial and Control EngineeringApplications 158 2.5 ... Neural Networks - Industrial and Control EngineeringApplications 146 According to the BP neuralnetwork model, the number of hidden neurons is initially chosen as 6, so the neuralnetwork ... algorithm in the optimization of hot pressing parameters ArtificialNeural Networks - Industrial and Control EngineeringApplications 162 The depicting effect of mentioned factors and their interactions...
... (as-received) ArtificialNeural Networks - Industrial and Control EngineeringApplications 168 Botlani-Esfahani. M, Toroghinejad. M. R. and Abbasi. Sh. (2009b) ArtificialNeuralNetwork Modeling the ... Precipitation in North Carolina. Water, Air, and Soil Pollution, 172, 167. ArtificialNeural Networks - Industrial and Control EngineeringApplications 176 for testing. For the training stage, ... Parameters of RSWS dynamic model ArtificialNeural Networks - Industrial and Control EngineeringApplications 192 Once the artificialneuralnetwork is trained, which means that all of the...
... propagation error. Learning of the network was carried out using 9 data points from the ArtificialNeural Networks - Industrial and Control EngineeringApplications 228 networks is given by ... 150-158(9) Part 3 Food Industry ArtificialNeural Networks - Industrial and Control EngineeringApplications 232 ANN was investigated, is given in Table 2. In regard to carcass classification ... iterative principle, which is similar to training of the network. 2.1 Feed-forward neuralnetwork Feed-forward neuralnetwork was the first type of ANN developed. In this network, the information...
... application of neural networks in computer aided design,Artificial Intelligence in Engineering, 5(1), 9-22.Currie, K.R., 1992, An intelligent grouping algorithm for cellular manufacturing, Comp. Ind. ... components for cellular manufacturing, keepinga concurrent engineering framework in mind. It utilizes two middle layers, as shown in Figure 4.4.The inputs for the network include design features of ... promise, in general, for reducing complexity in logistics, and for streamlining andsynergistic regrouping of many operations in the supply chain. This chapter provides a summary of neural network applications...
... artificialintelligence (AI) technique that has the potential of improving the product quality, increasing the effectevents in production, increasing autonomity and intelligence in manufacturing lines, ... of the network within the system. Various manufacturing processes includingmachining, arc welding, semiconductor, and hydroforming processes are considered for networks appli-cations. Finally, ... manufacturing processes. Some neural network applications to machining, arc welding, semiconductor fabrication, hydroforming, and hot plate rollingprocesses will be summarized in the following sections.12.6...