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This article offers a general approach to developing methods of determining operation tolerances for the parameters' values of memristor-based artificial neural networks (ANNM), as a system that constitutes an united physical and informational object implemented by the hardware and software learning facilities. While looking for a solution to the issues of analysis and synthesis of this system's tolerances,...
In machine learning, ensemble model is combining two or more models for obtaining the better prediction, accuracy and robustness as compared to individual model separately. Before getting ensemble model first we have to assign our training dataset into different models, after that we have to select the best model suited for our data sets. In this work we explored six machine learning parameter for...
This paper proposes a short-term energy price classification model using decision tree. The proposed model does not predict the exact value of future electricity price, but the class to which it belongs, established with respect to pre-specified threshold. This strategy is proposed since for some applications, the exact value of future prices is not required for the decision-making process. A feature...
Research community has recently put more attention to the Extreme Learning Machines (ELMs) algorithm in Neural Network (NN) area. The ELMs are much faster than the traditional gradient-descent-based learning algorithms due to its analytical determination of output weights with the random choice of input weights and hidden layer bias. However, since the input weights and bias are randomly assigned...
A bug in a software application may be a requirement bug, development bug, testing bug or security bug, etc. To prediet the bug numbers accurately is a challenging task. Advance knowledge about bug numbers will help the software managers to take decision on resource allocation and effort investments. The developers will be aware of the number of bugs in advance and can take effective steps to reduce...
A new technique for analysis of nonlinear effects in smart antenna array transmitter systems is presented. The analysis is founded on a new type of dual-input circuit behavioral model which allows nonlinear effects caused by antenna mutual-coupling and mismatch to be predicted under realistic wideband signal excitations. The model formulation enables direct interface with multi-port antenna S-parameters...
Wind power prediction is critical to power balance and economic operation of power system when connected to the grid. In order to improve prediction accuracy, NWP information of different positions and height are taken into consideration to predict wind power in wind farms. In this paper, similar day as the prediction day was searched as training sample at first. The key factors of multiposition NWP...
This paper attempts to predict the survival of patients using supervised machine learning techniques. To predict this task, the variables were identified and retrieved from the StatLib database. Both the artificial neural networks and linear regression models were used to perform the task. Experimental results, based on the classification accuracy were analysed from training and testing datasets....
Despite the high performance of artificial intelligent (AI) models for part quality prediction and control in machining operations, only well-known analytical models are commonly used in industry. This paper compares different analytical models with AI models for quality assurance in the fabrication of fluidic channels in micro-milling operations. The comparison of both types of models is conducted...
Taking the investigated data from 40 samples plots of natural secondary oak stand in Baotianman natural reserve for the research object, BP-ANN model was created by using relative diameter of tree as the input variable, and accumulated frequency of tree number as output variable. Through training and optimal seeking by the software of MATLAB, the idea network model was created. In the performance...
This paper presents the application of Artificial Neural Network (ANN) in development of an intelligent diagnosis system for selected psoriasis skin disease. Three major types of psoriasis images were captured with controlled environment and analyzed for color feature extraction from Red, Green and Blue(RGB) model. The images would be represented by their gaussian differential mean of each color component...
Bridge constriction in channels usually causes afflux which results in increase in backwater level well above the normal level and may possibly result in overflow on the flood plain surrounding the channel during flooding period. This paper uses Artificial Neural Network to predict the afflux based on the parameters including coefficient of frictions of main channel (nmc) and of floodplain (nfp),...
Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
This paper provides a method of discriminate analysis based on artificial neural network (ANN). 2-Class and multi-class discriminant analysis are separately discuss using Back Propagation network. The results of our study indicate that discriminate analysis based on ANN could classify the observation more accurately than the traditional methods.
In order to study the early warning of companies' financial risk, this paper used two models based on factor analysis, which are logistic regression and BP neural network. Finally, for the warming accuracy, BP neural network model is better than logistic regression model.
In order to improve the feature extraction efficiency of KMSE, we propose a novel KMSE algorithm. This algorithm assumes that the discriminant vector can be expressed as the linear combination of “significant nodes”, a subset of the training samples rather than all training samples. Determining the “significant nodes” based on the numerical analysis of the kernel matrix is the key of this method....
In this paper, we propose a compound pyramid model to predict protein secondary structure, where homology analysis and an improved support vector machine (SVM) technology are used for predicting protein secondary structure. The homology analysis is based on BP network model which uses pair-wise sequence alignment, and SVM classification considers the physical and chemical properties of amino acids...
In order to solve the conflict between accuracy and speed in dynamic quantitative weighing process, the neural network PID controller is designed by means of the neural network theory combined with the PID control theory for the dynamic quantitative weighing system,and proposed the new intelligent control strategy. PID (Proportion Integration Differentiation) controllers are used in a large number...
Sixty fabrics with elasticity were made up into two hundred fifty eight samples to evaluate the touch and pressure sensation in this paper. Twelve female were selected as subjects for wear trials. Nineteen physical parameters of fabrics were measured by FAST system. Four principal factors were obtained through data reduction with factor analysis method. The objective evaluation model of wearing touch...
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