artificial neural networks in java

... continued interest in the use of neural network tools in scientific inquiry In the opening chapter, an introduction and brief history of computational neural network models in relation to brain ... Methodological issues in building, training, and test-ing artificial neural networks in ecological applications Ecological Modelltest-ing 195: 83–93. Parker, X., and Newsom, X 1998 Sense and the single neuron: ... background for the remainder of this book, especially Chapter 3, given that training of neural networks is discussed in detail 2.2 feedforwArd neurAl networks Feedforward neural networks are arguably

Ngày tải lên: 14/03/2018, 15:08

... Networks, Artificial Neural Networks in Trang 3626 3 Artificial Neutral Networks understanding of the problem to be solved and training data is ily available [67] In general, neural networks can be ... comprising of densely interconnected adaptive processing units These networks are fine-grained parallel implementation of nonlinear static or dynamic systems [63, 64] Neural networks are intended ... KhareProfessor in Civil Engineering Indian Institute of Technology Delhi Atlantic LNG Chair Professor in Environmental Engineering, University of West Indies E-mail: mukeshk@civil.iitd.ernet.in &

Ngày tải lên: 05/09/2020, 11:45

... Trang 1Studies in Computational Intelligence 557Combining Development and Learning in Artificial Neural Networks Trang 2Studies in Computational Intelligence Volume 557Trang 3About ... maininterest with this book, as exposed in this introductory chapter, resides in the potential to use brain-inspired mechanisms for engineering challenges. 1.3.1 Challenges in Large-Scale Brain ... representations 4.1 Deep Learning With the advent of deep learning, neural networks have made headlines again both in the machine learning community and publicly, to the point that “deep networks”could be

Ngày tải lên: 12/04/2019, 00:12

... Trang 1Studies in Computational Intelligence 557Combining Development and Learning in Artificial Neural Networks Trang 2Studies in Computational Intelligence Volume 557Trang 3About ... maininterest with this book, as exposed in this introductory chapter, resides in the potential to use brain-inspired mechanisms for engineering challenges. 1.3.1 Challenges in Large-Scale Brain ... representations 4.1 Deep Learning With the advent of deep learning, neural networks have made headlines again both in the machine learning community and publicly, to the point that “deep networks”could be

Ngày tải lên: 05/11/2019, 14:31

... between the input and output data sets in the following way: during the training phase, input/output data pairs, called training data, are introduced into the neural network The difference be-tween ... GUA) Training data are listed inTables 1 and 2for combinations I and II, respectively Neural networks were trained using different numbers of neurons (2–20) in the hidden layer and training cycles ... problem was over-fitting or over-training, evident by an increase in the test error Neu-ral networks were trained using different numbers of hidden neurons (2–20) and training cycles (150–500)

Ngày tải lên: 13/01/2020, 22:36

... difficult perform online calculations ANN is in need of initial line data for training Extensive off-line simulation is performed so as to acquire a large enough set of training data to represent ... clear signal for dataset learning So, this paper did mining of fault-on input features (Vbus, Pload, Qload, Pflow, Qflow) as a database for training neural networks The output variables ... lines Load level is one hundred percent rated load Fault types are balanced three-phase, single line to ground, line to line, double line to ground at buses and along transmission lines Setting

Ngày tải lên: 12/02/2020, 19:45

... Trang 10Application of artificial neural network to optimize drilling parameters for infill wells in Cuu Long basin Nguyen Tien Hung1*, Nguyen Van Thinh1, Nguyen The Vinh1, Vu Hong Duong1 1 ... providing optimal drilling parameters to improve drilling efficiency We use data obtained from 03 infill wells in Cuu Long basin, including 5 input parameters, which are weight on bit (WOB), rotational ... (ROP) to train the network During the training of the network, the research team changes the number of neurons in the hidden layer to find the optimal model The proposed artificial neural network

Ngày tải lên: 14/03/2024, 18:42

... Trang 1ARTIFICIAL NEURAL NETWORKS ͳ INDUSTRIAL AND CONTROL ENGINEERING APPLICATIONSEdited by Kenji Suzuki Trang 2Artificial Neural Networks - Industrial and Control Engineering ApplicationsEdited ... neural networks (ANN) Various leveling action point affecting variables were selected as inputs for training the artificial neural networks, which was aimed to optimize the auto-leveling by limiting ... 2011 Printed in India A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Artificial Neural Networks - Industrial

Ngày tải lên: 20/06/2014, 00:20

... Application of Artificial Neural Networks in Textiles and Clothing Industriec over Last Decades 25 Trang 4Review of Application of Artificial Neural Networks in Textiles and Clothing Industriec ... 1000 spindle hours; by means of inputs including the processing parameters such as fiber properties, spinning method, and process variables influencing on the yarn properties and spinning performance ... simulated the spinning of the worsted yarn with the high coincidence using the processing data in the mills based on the artificial neural networks and grey superior analysis (Yin & yu, 2007)

Ngày tải lên: 20/06/2014, 00:20

... training and testing, and all the networks were trained using the same learning rate (0.5 ® 0.01) and momentum term (0.5 → 0.1) The 311 samples produced were divided in two groups: a training ... network by linear connection with linear or nonlinear transformations The weights were determined by training the neural nets Once the ANN was trained, it was used for predicting new sets of inputs ... Data Modeling using Classical Models and Neural Networks Chemical Engineering Science, Vol.55, pp 2767-2778, ISSN 0009-2509 Chattopadhyay, R & Guha, A (2004) Artificial Neural Networks:

Ngày tải lên: 20/06/2014, 00:20

... 1st 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 ... ANN training is split into two steps In the first training, only the average line intensity of the oxide of interest is fed to the network This average intensity is duplicated to several input ... the 1 st training Weights & biases from the 2 nd training Weights & biases from the 3 rd training Weights & biases from the 4 th training Trained ANN Fig 8 Sequential training diagram

Ngày tải lên: 20/06/2014, 00:20

... of Artificial Neural Networks to the Investigation of Aging Dynamics in 7175 Aluminium Alloys Materials Science and Engineering C, Vol.3, No.1, (October 1995), pp 39-41, ISSN 0928-4931 Srinivasan, ... data in compositions optimization According to the theory of the BP neural network, the computing process is programmed with neural network toolbox in MATLAB Training function is using ‘trainlm’ ... material The training sample data of BP neural network are the experimental data (Table 2) The input is the hot pressing parameters, including the sintering temperature and holding time And the

Ngày tải lên: 20/06/2014, 00:20

... concentration divides the figure into three regions include finer, mild and coarser grain structures This figure also indicates that increasing Si content, increases grain size This is because silicon ... criterion (SPSS Inc., 2004) 3.2 Adaptive neuro fuzzy inference system In the artificial intelligence field, the term “neuro-fuzzy” refers to combinations of artificial neural networks and fuzzy ... by decreasing finishing temperature, the final tensile strength increases Inter-pass recrystallization and grain growth prevention my causes this effect (Preloscan et al., 2002) The influence

Ngày tải lên: 20/06/2014, 00:20

... set of inputs • Learning rate: A learning rule, which changes the connection weights of the network in response to the example inputs and desired output to those inputs The training of neural ... products during processing and distribution ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine ... space, called a baking curve, along which the Trang 12bake colour changes during the baking process Combining these results, an automated bake inspection system with artificial neural networks that

Ngày tải lên: 20/06/2014, 00:20

... certain similarity, for example, directly extract training samples result in containing many redundant data So they need preliminary sorting It contain Trang 13Artificial Neural Networks - Industrial ... 15Artificial Neural Networks - Industrial and Control Engineering Applications 248 It adopts the training and emulating alternate work model to avoid the net excess training After the training samples ... and improve the training speed of the network[22] The BP neural network training process used in this article is shown in Fig 3 Fig 3 The training flow chart of BPNN Input training samples U and

Ngày tải lên: 20/06/2014, 00:20

... sometimes called a MADALINE for Many ADALINEs Note that the figure on the right defines an S-length output vector a The Widrow-Hoff rule can only train single-layer linear networks This is not much ... disadvantage, however, as single-layer linear networks are just as capable as multilayer linear networks For every multilayer linear network, there is an equivalent single-layer 1,1 1 1,2 2 Like ... into two categories However, ADALINE can classify objects in this way only when the objects are linearly separable Thus, ADALINE has the same limitation as the perceptron 5.2 Networks with linear

Ngày tải lên: 20/06/2014, 00:20

... However, missing out extreme states in the operation may result in a lack of training information Neural networks cannot extrapolate states that are not covered by the training data as shown in the ... combination of ANN includes specialised networks trained for subtasks combined with others resulting in a superior task solution Task distribution helps in overcoming generalisation problems by including ... cycles - decreasing correlation with increasing SOI timing (black line) and overcoming calibration variation with multiple training cycles (blue line) Trang 15general decreasing trend is recognized

Ngày tải lên: 20/06/2014, 00:20

... network is trained as the inverse of the process: g− θ Z will be used instead of g− 1(θ,Z N) To obtain the inverse model in the generalized training method, a network is trained off-line to minimize ... continuous non-linear functions to within an arbitrarily small error margin Hiddenlayer j Inputlayer i Outputlayer Fig 3 A two layer artificial neural network 3.2 The training agorithm In developing ... chapter Before considering the actual control system, an inverse model must be trained There are tow ways of training the model; generalized training and the specialized training This chapter uses

Ngày tải lên: 20/06/2014, 00:20

... programming logic IDC is in charge of functions such as current information displaying, monitoring control, and machine status reasoning Details of these functions are given in the following section ... off-line approach unrealistic and inefficient for a fast-changing manufacturing environment (Singh & Kazzaz, 2003) Over the past few decades technologies in machine condition monitoring and ... new kinds of information that may not have been directly associated with the traditional maintenance methodologies Therefore, how to integrate this new information into maintenance planning to

Ngày tải lên: 20/06/2014, 00:20

... tracking for both configurations compared to each other after the training was finished for the X coordinate 474 Artificial Neural Networks - Industrial and Control Engineering ... 478 Artificial Neural Networks - Industrial and Control Engineering Applications. .. Actuator Using Neural Networks With Fuzzy Capabilities, European Symposium on Artificial ... calculated by: 468 Artificial Neural Networks - Industrial and Control Engineering Applications E= 1 ∑ ( dK − OK )2 2 K (22) Learning comprises changing weights so as to minimize the

Ngày tải lên: 20/06/2014, 00:20