neural network training procedure

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Training Neural Network Elements Created From Long Shot Term Memory

Training Neural Network Elements Created From Long Shot Term Memory

So, behind the transformation of RNN to a corresponding simulation model network optimization i.e. training is delivered through the simulation model. This scheme is presented on Fig.4. Between the simulation model and the network RNN there is correspondence appropriate entities. The scheme (Fig.4) contains the auxiliary devices for automatic optimization of procedure of training ie: random number generator G1, generators G2 and G3 training pairs with a sinhronisation possibility, and a decision block DB for the completion of certain procedures. The presented methodology can be characterized as a blackbox method 16
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The use of adversaries for optimal neural network training

The use of adversaries for optimal neural network training

The input data is pre-processed by NB to transform each variable into a Gaussian dis- tribution. The batch-size was 100, and NB was trained over 150 epochs using the Broy- den–Fletcher–Goldfarb–Shanno algorithm (see [12] for more information). Regularisation is employed using the ‘Bayesian regularisation procedure’ (see [13] for details on the Neu- roBayes algorithm). During training NeuroBayes employs pruning and removal of the least important weights to prevent over-training.

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Evaluation of Stiffened End Plate Moment Connection through Optimized Artificial Neural Network

Evaluation of Stiffened End Plate Moment Connection through Optimized Artificial Neural Network

measure of fitness, such as sum square error of residual, becomes minimized. This procedure must be done after adjusting the network topology and inputs of models, and its performance highly affected by the mentioned items. In this case, the artificial neural network training exercise becomes an unconstrained, nonlinear optimization prob- lem in the weight space, network topology and model inputs, so an appropriate algorithm may be used to solve this problem. It may be known as the most important topic of system identification to achieve the best per- formance of simulation between different model features. One of the best methods to optimize these complicated problems is suing heuristic approaches such as Genetic algorithm (GA). GA is considered to be a heuristic, pro- babilistic, combinatorial, search-based optimization tech- nique based on the biological Darwinian approach in na- tural evolution developed by Holland (1975) [16] and developed by Goldberg (1989) in its robustness for solv- ing nonlinear, optimization problems [17,18]. Montana and Davis (1989) and Maniezzo (1994) applied SGA in training a back-propagation neural network [19-21]. In the current paper, GA has been used to optimize combi- nation of model inputs, ANN topology (number of nodes in hidden layer) and parameters of learning algorithms. To achieving the best performance of ANN modeling, the next steps have been done successively,
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Evaluating Neural Machine Translation in English Japanese Task

Evaluating Neural Machine Translation in English Japanese Task

Attentional models are first proposed in the field of computer vision, which allows the re- current network to focus on a small portion in the image at each step. The internal state is updated only depends on this glimpse. Soft- attention first evaluates the weights for all pos- sible positions to attend, then make a weighted summarization of all hidden states in the en- coder. The summarized vector is finally used to update the internal state of the decoder. Contrary to hard-attention mechanism which selects only one location at each step and thus has to be trained with reinforce learning tech- niques, soft-attention mechanism makes the computational graph differentiable and thus able to be trained with standard backpropa- gation.
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Using Neural Networks for Corrosion Inhibition Efficiency Prediction during Corrosion of Steel in Chloride Solutions

Using Neural Networks for Corrosion Inhibition Efficiency Prediction during Corrosion of Steel in Chloride Solutions

In spite of the huge success that has been attributed to the use of computational chemistry in corrosion studies, most of the ongoing research on the inhibition potential of organic inhibitors is restricted to laboratory work. The quantitative structure inhibition (activity) relationship (QSAR) approach is an effective method that can be used together with experimental techniques to predict inhibitor candidates for corrosion processes. The study has demonstrated that the neural network can effectively generalize correct responses that only broadly resemble the data in the training set. The neural network can now be put to use with the actual data, this involves feeding the neural network with several quantum chemical descriptors as dipole moment, highest occupied (HOMO) and lowest unoccupied (LUMO) molecular orbital energy, energy gap, molecular area and volume. The neural network will produce almost instantaneous results of corrosion inhibition efficiency.
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Snapshot Imaging Spectrometry in the Visible and Long Wave Infrared.

Snapshot Imaging Spectrometry in the Visible and Long Wave Infrared.

To produce calibrated spectral images, neural networks (NNs) were investigated as an alternative to conventional Fourier- or linear operator-based techniques. Often, Fourier transformations, in combination with a phase correction algorithm (e.g., Mertz or Foreman), are used to construct calibrated spectral images [46-48]. Alternatively, linear operators can be used to represent the system’s measurement matrix, which is then inverted to solve for the input spectrum [49]. In this work it was determined that (1) neural networks eliminate the need for implementing phase correction methods – it is effectively encoded into the network; and (2) they reduce spatial artifacts observed in the calibrated spectral slices. Depicted in Fig. 16 are two datacubes that illustrate these spatial artifacts. Fig. 16 (a) illustrates data that were measured using the SHIFT spectrometer, observing a spatially uniform and spectrally broadband source, and calibrated using a linear operator with expectation maximization (EM) [49, 50]. Meanwhile, Fig. 16 (b) was obtained using a past version of the SHIFT and calibrated using the Fast Fourier Transform (FFT) algorithm with Mertz phase correction [21].
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Implementation of Neural Network Back Propagation Training Algorithm on FPGA

Implementation of Neural Network Back Propagation Training Algorithm on FPGA

This work presents the implementation of trainable Artificial Neural Network (ANN) chip, which can be trained to implement certain functions. Usually training of neural networks is done off-line using software tools in the computer system. The neural networks trained off-line are fixed and lack the flexibility of getting trained during usage. In order to overcome this disadvantage, training algorithm can implemented on-chip with the neural network. In this work back propagation algorithm is implemented in its gradient descent form, to train the neural network to function as basic digital gates and also for image compression. The working of back propagation algorithm to train ANN for basic gates and image compression is verified with intensive MATLAB simulations. In order to implement the hardware, verilog coding is done for ANN and training algorithm. The functionality of the verilog RTL is verified by simulations using ModelSim XE III 6.2c simulator tool. The verilog code is synthesized using Xilinx ISE 10.1 tool to get the netlist of ANN and training algorithm. Finally the netlist was mapped to FPGA and the hardware functionality was verified using Xilinx Chipscope Pro Analyzer 10.1 tool. Thus the concept of neural network chip that is trainable on-line is successfully implemented.
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Design of Low Noise Amplifier of IRNSS using ANN

Design of Low Noise Amplifier of IRNSS using ANN

Paper presents a Neural Network Modeling approach to microwave LNA design. To acknowledge the specifications of the amplifier, Mobile Satellite Systems are analyzed. Scattering parameters of the LNA in the frequency range 0.5 to 18 GHz are calculated using a Multilayer Perceptron Artificial Neural Network model and corresponding Smith charts and Polar charts are plotted as output to the model. This paper describes the design and measurement of a medium power amplifier (MPA) using 0.15µm GaAs PHEMT technology for wireless application. At 2.4 GHz and 3.0 V of VDS, a fabricated MPA exhibits a P1dB of 15.20 dBm, PAE of 12.70% and gain of 9.70 dB. The maximum current, Imax is 84.40mA and the power consumption for this device is 253.20mW. The die size of this amplifier is 1.2mm x 0.7mm.
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Artificial Intelligence Neural Networks Applications in Forecasting Financial Markets and Stock Prices

Artificial Intelligence Neural Networks Applications in Forecasting Financial Markets and Stock Prices

Abstract: The interest in using artificial neural networks (ANN’s) for forecasting has led to a tremendous surge in research activities over time. Artificial Neural Networks are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. Forecasting problems arise in so many different disciplines and the literature on forecasting using ANN’s is scattered in so many diverse fields that it is hard for a researcher to be aware of all the work done to date in the area. There is an extensive literature in financial applications of ANN’s. Naturally forecasting stock price or financial markets has attracted considerable interest and it has been one of the biggest challenges. This paper reviews the history of the application of artificial neural networks for forecasting future stock prices. From the introduction of the back-propagation algorithm in 1980’s for training an MLP neural network by Werbos, who used this technique to train a neural network and claimed that neural networks are better than regression methods and Box-Jenkins model in prediction problems through the application of such technics to financial markets forecasting by pioneers in the field like White, Kimoto and Kamijo to the more recent studies of stocks prices in not only the biggest capital markets but also in some emerging and illiquid markets, we will look at the progress made in the past more than twenty five years of research.
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Optimizing Process Parameters of Rotary Furnace using Bio fuels: An Interactive ANN Approach

Optimizing Process Parameters of Rotary Furnace using Bio fuels: An Interactive ANN Approach

The proposed approach of using Artificial Neural Networks as an optimization tool solves a very challenging problem of selecting the optimal process output of melting rate for the production of homogenous high quality castings. The forward ANN gives a maximum error of 0.087314 which is quite admissible and accepted by decision maker, the polynomial curve gives an error of maximum .035 and is quite satisfactory. Although for the backward ANN model has a problem that it has only 3 input and 4 output which is quite difficult to implement in a model, but our model gives satisfactory result with a maximum error of 0.79703 and the polynomial curve has an maximum error of .037 which is accepted by decision maker as it is the maximum error among 21 output sets. The decision maker can directly interact with the networks and identify desired process inputs and corresponding process outputs and vice versa through forward and reversed mapping networks. The optimum result obtained is almost instantaneous that guides the decision maker to select best rotary furnace parameters. Although the adopted approach is used for optimizing rotary furnace parameters in this work, it can be applied to other metal cutting or removing operations with the same accuracy [6,7].
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A Predictive Model for Corrosion Inhibition of Mild Steel by Thiophene and Its Derivatives Using Artificial Neural Network

A Predictive Model for Corrosion Inhibition of Mild Steel by Thiophene and Its Derivatives Using Artificial Neural Network

In spite of the huge success that has been attributed to the use of computational chemistry in corrosion studies, most of the ongoing researches on the inhibitory potentials of organic inhibitors are restricted to laboratory work. DC polarization method used to ascertain the instantaneous inhibition efficiency of the thiophene derivatives. QSAR approach is still an effective method that can be used together with the experimental techniques to predict inhibitor candidates for corrosion process. The study has demonstrated that the neural network can effectively generalize correct responses that only broadly resemble the data in the training set. The neural network can now be put to use with the actual data, this involves feeding the neural network the values for Hammett constants, dipole moment, HOMO energy, LUMO energy, energy gap, molecular area and volume. The neural network will produce almost instantaneous results of corrosion inhibitor efficiency. The predictions should be reliable, provided the input values are within the range used in the training set.
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Using artificial neural network to monitor and predict induction motor bearing (IMB) failure

Using artificial neural network to monitor and predict induction motor bearing (IMB) failure

The purpose of this paper is to develop an appropriate artificial neural network (ANN) model of induction motor bearing (IMB) failure prediction. Acoustic emission (AE) represented the technique of collecting the data that was collected from the IMB and this data were measured in term of decibel (dB) and Distress level. The data was then used to develop the model using ANN for IMB failure prediction model. An experimental rig was setup to collect data on IMB by using Machine Health Checker (MHC) Memo assist with MHC Analysis software. In the development of ANN modeling, two networks were tested; Feedforward Neural Network (FFNN) and Elman Network for the performance of training, validation and testing with training algorithm, Levenberg-Marquardt Back-propagation and the suitable transfer function for hidden node and output node was logsig/purelin combination. The results show the performance of Elman network was good compared to FFNN to predict the IMB failure.
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Face Recognition using Rectangular Feature

Face Recognition using Rectangular Feature

In this paper, we generalize the things that are useful for the face detection and recognition based on the rectangular feature. The rectangular feature is used for the face detection purpose and feature extraction. PCA is also used for calculating feature vector to make matrix of the face data. These matrix is used for the compare the values to the other matrix and formed correlation matrix. Images are converted in to columns in the form of data that data are used for input to our classifier. Inputs are in the form of numerical values our proposed method gives the better results in the form of recognition rate that is in percentage. For classifying we used RBFNN as classifier. In rectangular feature we consider eyes, lips, nose as a rectangular feature for this we did normalization process. On this rectangular feature we applied PCA algorithm for calculating feature vector. We used different size of the face image ORL database is used for the training and testing of the system. The size is 100x100, 150x150 and 200x200. Based on the experiment we found 200x200 is gives better recognition rate and fifteenth number of feature gives good recognition rate.
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A NEURAL NETWORK MODEL FOR SHORT TERM PREDICTION OF SURFACE OZONE AT TROPICAL CITY

A NEURAL NETWORK MODEL FOR SHORT TERM PREDICTION OF SURFACE OZONE AT TROPICAL CITY

Neural Networks are signal processing systems that attempt to emulate the behavior of biological nervous systems by providing a mathematical model of combination of numerous basic blocks called neurons connected in a network. It is remotely analogous to living nervous system and hence its name. One can think of neural networks as an extended form of regression which has the properties of

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Swarm-based Algorithms for Neural Network Training

Swarm-based Algorithms for Neural Network Training

Tube, Diabetes in Pima Indians, Sin Times Sin, and Rise Time Servomechanism. PSO was shown to be more robust when there is a high number of local minima [33]. [8] applied PSO to training ANNs while also applying PSO variants, backpropagation variants, and Hybrid approaches between PSO and backpropagation using backprop- agation variants as a local search mechanism. It was shown that PSO was successful when applied to the Diabetes dataset [8]. A comparison was performed in [22] where multiple ANN training approaches were applied to four classification datasets as well as an e-Learning dataset. These approaches included PSO, Genetic Algorithm, bat algorithm, and Levenberg-Marquardt algorithm. It was found in [22] that the bat algorithm was more useful when applied to these datasets. [43] investigates the over- fitting behaviour of PSO trained ANNs. [43] found that the PSO topology influenced the overfitting behaviour, as well as the use of bounded activation functions. [43] also witnessed non-convergent behaviour in the PSO swarm which was attributed to the use of bounded activation functions. When unbounded activation functions were used, it was found that the PSO swarm converged while overfitting behaviour was drastically reduced [43].
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Performance comparison of three artificial neural network methods for classification of electroencephalograph signals of five mental tasks

Performance comparison of three artificial neural network methods for classification of electroencephalograph signals of five mental tasks

The result showed the performance of neural network with resilient back propagation training method, support vector machine and radial bases function Neural Net- work for classifying of mental tasks w.r.t baseline. RBF (Radial Basis Function) neural network method has best performance among all the classifiers for classification of mental tasks w.r.t baseline. By using RBF Neural Network 100% accuracy was obtained. While classifica- tion, Resilient Back Propagation training method showed better performance than other (Gradient Descent method, Levenberg-Marquardt, Conjugate Gradient De- scent and Gradient Descent Back Propagation with movementum) back propagation training methods. The main conclusion is that the Radial baisis function net- work was found to be most suitable in various applica- tions of BCI systems.
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To Improve the Performance of Secondary Structure Prediction by Soft Computing Technique

To Improve the Performance of Secondary Structure Prediction by Soft Computing Technique

Abstract— Proteins, one of the basic building blocks of all the organisms, need exploratory techniques to predict its complex structures. Machine learning technique such as neural network has been widely used in predicting secondary structures of amino acid sequences. Today, the main aim is to improve the performance of secondary structure predictions by learning a predictive model trained on known structures. Multi-layered feed forward neural network model is trained with the help of hidden layer and by varying sliding window size to determine optimal window size giving highest accuracy. Binary bit encoding scheme is used to encode input training sequence in 3 state DSSP codes. The current efficiency achieved through this technique has reached up to 70% .The experimental results reveal that the following proposed algorithm yields an improved performance with an accuracy of 80%.
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Shape Optimization of Pedestals Using Artificial Neural Network

Shape Optimization of Pedestals Using Artificial Neural Network

changing methods, in which thickness is changed. Jose (1990) and Sibal (1992) used this technique to find optimal shape of the downstream side of dams to eliminate tensile stress from the heel of the dams. In these problems co-ordinates of the nodes of the design points were varied in prescribed directions. Pathak (2000) used design elements, fuzzy set theory and artificial neural networks in a gradientless method of shape optimisation. Zhixue Wu (2005) presented an efficient gradientless shape optimization approach for minimizing stress concentration factor. Hsu (1993) developed a new method for optimization called as ‘curvature function method’. They solved various problems such as cantilever beam, fillet and torque arm. Ghoddosian (1998) extended curvature function method to find optimum shape of shell structures and successfully solved one circular and one spherical shell problems. The pattern transformation method of Oda and Yamazaki (1984) is a technique of transforming the shape of the boundary based on the stress ratio in the boundary finite elements. Umetani and Hirai (1979) used stress ratio approach whereas Tada and Seguchi (1981) considered strain energy ratios for shape optimization. Sehgal, et al. (1999) optimized bracket problem using Boundary Element Method (BEM) and zero-order approach. In recent years application of artificial intelligence based techniques have taken an impor tant place in structural engineering and shape optimization is not untouched to that. Most important among them is evolutionary method, genetic algorithms (GA), neural networks etc. Nicholas Ali (2003) reported shape optimization of very large planer and space problems using GA. The proposed clubbing of FEA and GA finds lighter and reasonable structural design. Zhang(2005) repor ted application of meshless method and genetic algorithms for shape
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Discriminative Training of a Neural Network Statistical Parser

Discriminative Training of a Neural Network Statistical Parser

Much recent work has investigated the applica- tion of discriminative methods to NLP tasks, with mixed results. Klein and Manning (2002) argue that these results show a pattern where discriminative probability models are inferior to generative probability models, but that im- provements can be achieved by keeping a gener- ative probability model and training according to a discriminative optimization criteria. We show how this approach can be applied to broad coverage natural language parsing. Our estima- tion and training methods successfully balance the conflicting requirements that the training method be both computationally tractable for large datasets and a good approximation to the theoretically optimal method. The parser which uses this approach outperforms both a genera- tive model and a discriminative model, achiev- ing state-of-the-art levels of performance (90.1% F-measure on constituents).
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SecureNN:  Efficient   and  Private  Neural  Network  Training

SecureNN: Efficient and Private Neural Network Training

Comparison with prior work. We remark that this is the first work that implements training for networks B and C and these networks are much larger and give much higher accuracy (> 98%) than network A (93%) considered by prior work [33]. The only prior work to consider secure training of neural networks is SecureML [33] that provides computationally secure protocols against a single semi-honest adversary in the 2-server and 3-server models for Network A. Compared to their 2-server protocols, we give an improvement of 79× and 553× in the LAN and WAN settings, respec- tively. They implement their 3-server protocol in the LAN setting only, and our protocols outperform this by 7×. SecureML also split their protocols into an offline (independent of data) and online phase. Even when comparing their online time only with our total time, we obtain a 2.8× improvement over their 3-server pro- tocols. Our drastic improvements can be attributed to a roughly 8× improvement in communication complexity for computing non-linear functions and the elimination of expensive oblivious transfer protocols from the of- fline phase, which are another major source of overhead. Secure Inference. Next, we consider the problem of secure inference for the same networks when the trained model is secret shared between the servers. For the smallest network A, a single prediction takes roughly 0.04s and 2.43s in the LAN and WAN settings, respectively. For the largest network C, a single predic- tion takes 0.23s in LAN and 4.08s in the WAN setting. As is observed by previous works as well, doing batch predictions is much faster in the amortized sense than doing multiple predictions serially. For instance, for network C, a batch of 128 predictions take only 10.82s in the LAN and 30.45s in the WAN setting.
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