This paper aims to enhance the performance of a cascade-forward neural network (CFNN) model predict output power photovoltaic (PV) module. improvement is conducted by optimizing number hidden neurons using genetic algorithm (GA). The optimization carried out minimize value root mean square error (RMSE) between actual and predicted PV power. CFNN-based GA evaluated five statistical term terms; n...

Proper analysis of building energy performance requires selecting appropriate models for handling complicated calculations. Machine learning has recently emerged as a promising effective solution solving this problem. The present study proposes novel integrative machine model predicting two parameters residential buildings, namely annual thermal demand (DThE) and weighted average discomfort deg...

This paper proposes a new architecture — Attentive Tensor Product Learning (ATPL) — to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially us...

the static pull-in instability of beam-type micro-electromechanical systems is theoretically investigated. two engineering cases including cantilever and double cantilever micro-beam are considered. considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent euler-bernoulli beam model is used based on a modified couple stress theory, c...

A comparative study of artificial neural network (ANN) and multiple regression is made to predict the fat tail weight of Balouchi sheep from birth, weaning and finishing weights. A multilayer feed forward network with back propagation of error learning mechanism was used to predict the sheep body weight. The data (69 records) were randomly divided into two subsets. The first subset is the train...

In this paper the RWTH large vocabulary continuous speech recognition (LVCSR) systems developed for the IWSLT2016 evaluation campaign are described. This evaluation campaign focuses on transcribing spontaneous speech from Skype recordings. State-of-the-art bidirectional long shortterm memory (LSTM) and deep, multilingually boosted feed-forward neural network (FFNN) acoustic models are trained a...

Minimizing dilution is essential in open stope mine design as excessive unplanned can compromise the operation's profitability. One of main challenges associated with empirical graph method used to stopes how determine boundary zones objectively. Hence, this paper explores implementation machine learning classifiers bridge gap conventional method. Stope performance data consisting (unplanned di...