Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of applications. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thoroughly examined: the structu...

Deep learning methods have recently achieved great empirical success on machine translation, dialogue response generation, summarization, and other text generation tasks. At a high level, the technique has been to train end-to-end neural network models consisting of an encoder model to produce a hidden representation of the source text, followed by a decoder model to generate the target. While ...

In the present work, a hyperelastic constitutive model based on neural networks is proposed which fulfills all common conditions by construction, and in particular, applicable to compressible material behavior. Using different sets of invariants as inputs, potential formulated convex network, thus fulfilling symmetry stress tensor, objectivity, symmetry, polyconvexity, thermodynamic consistency...

If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, applying neural networks a practical guide always becomes the most want...

In its current state, the simulator provides all the above. It is intended as a research tool for investigating spiking neural systems, and particularly their use in spike based auditory processing. The basic mode of operation is very simple: once the network has been set up and the input files determined, the network is simulated using a fixed time-step for a predetermined length of time. The ...

To determine the lubrication oil consumption change of linear motion guide under some mileage, this study designs a test machine to fasten the linear motion guide. The grey prediction model GM(1,1) and neural network are employed for comparison and exploration. Through this study we can understand the differences in prediction of lubrication oil consumption between neural network and grey theor...

An artificial neural network (ANN) model is developed for the analysis, simulation, and prediction of the austenite reversion in the thermo-mechanical treatment of 304L austenitic stainless steel. The results of the ANN model are in good agreement with the experimental data. The model is used to predict an appropriate annealing condition for austenite reversion through the martensite to austeni...

in this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. scheduling algorithms play an important role in grid computing, parallel tasks scheduling and sending them to appr...