Gating Artificial Neural Network Based Soft Sensor

This work proposes a novel approach to Soft Sensor modelling, where the Soft Sensor is built by a set of experts which are artificial neural networks with randomly generated topology. For each of the experts a meta neural network is trained, the gating Artificial Neural Network. The role of the gating network is to learn the performance of the experts in dependency on the input data samples. The final prediction of the Soft Sensor is a weighted sum of the individual experts predictions. The proposed meta-learning method is evaluated on two different process industry data sets

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure

Neural Networks Architecture Evaluation in a Quantum Computer

In this work, we propose a quantum algorithm to evaluate neural networks architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The proposed algorithm is based on a quantum associative memory and the learning algorithm for artificial neural networks. Unlike conventional algorithms for evaluating neural network architectures, QNNAE does not depend on initialization of weights. The proposed algorithm has a binary output and results in 0 with probability proportional to the performance of the network. And its computational cost is equal to the computational cost to train a neural network

A Neural-CBR System for Real Property Valuation

In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However, individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case- Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case- Based Reasoning systems

Artificial Neural Network in Cosmic Landscape

In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.Comment: v2, add some new content

Neural networks for impact parameter determination

Accurate impact parameter determination in a heavy-ion collision is crucial for almost all further analysis. We investigate the capabilities of an artificial neural network in that respect. First results show that the neural network is capable of improving the accuracy of the impact parameter determination based on observables such as the flow angle, the average directed inplane transverse momentum and the difference between transverse and longitudinal momenta. However, further investigations are necessary to discover the full potential of the neural network approach

CLASSIFICATION OF FEATURE SELECTION BASED ON ARTIFICIAL NEURAL NETWORK

Pattern recognition (PR) is the central in a variety of engineering applications. For this reason, it is indeed vital to develop efficient pattern recognition systems that facilitate decision making automatically and reliably. In this study, the implementation of PR system based on computational intelligence approach namely artificial neural network (ANN) is performed subsequent to selection of the best feature vectors. A framework to determine the best eigenvectors which we named as ‘eigenpostures’ of four main human postures specifically, standing, squatting/sitting, bending and lying based on the rules of thumb of Principal Component Analysis (PCA) has been developed. Accordingly, all three rules of PCA namely the KG-rule, Cumulative Variance and the Scree test suggest retaining only 35 main principal component or ‘eigenpostures’. Next, these ‘eigenpostures’ are statistically analyzed via Analysis of Variance (ANOVA) prior to classification. Thus, the most relevant component of the selected eigenpostures can be determined. Both categories of ‘eigenpostures’ prior to ANOVA as well as after ANOVA served as inputs to the ANN classifier to verify the effectiveness of feature selection based on statistical analysis. Results attained confirmed that the statistical analysis has enabled us to perform effectively the selection of eigenpostures for classification of four types of human postures