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17 results for “hidden neurons”
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Research ArticleOpen accessEvery word in the titleEffect of Hidden Layer Neurons on the Classification of Optical Character Recognition Typed Arabic NumeralsNidal F. Shilbayeh, Mahmoud Z. IskandaraniProblem statement: The effect of varying the number of nodes in the hidden layer and number of iterations are important factors in the recognition rate. In this paper, a novel and effective criterion based on Cross Pruning (CP) algorithm is proposed to optimize number of hidden neurons and number of iterations in Multi Layer Perceptron (MLP) neural based recognition system. … …Journal of Computer Science, Vol. 4, Issue 7 · 31 Jul 2008 · 7,556 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEvaluation of Surface Drag Interval in the Variable SituationsParisa Ashrafi, Amir Hossein Ashrafi, Hossein Gholami et al.Tools applied in different fields of engineering are artificial neural networks to model non-linear systems which are efficient and useful. Artificial neural networks consist of input and output layers with one or more hidden layers between them. In fact, one or more processing elements or neurons are used in each layer. Input layer neurons are the independent variables studied and … …American Journal of Engineering and Applied Sciences, Vol. 15, Issue 1 · 12 Mar 2022 · 6,001 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAccurate Wavelet Neural Network for Efficient Controlling of an Active Magnetic Bearing SystemYoussef Harkouss, Souhad Mcheik, Roger AchkarProblem statement: The synthesis of a command by the neural network has an excellent advantage over the classical one such as PID. This study presented a fast and accurate Wavelet Neural Network (WNN) approach for efficient controlling of an Active Magnetic Bearing (AMB) system. Approach: The proposed approach combined neural network with the wavelet theory. Wavelet theory may be exploited … …Journal of Computer Science, Vol. 6, Issue 12 · 9 Nov 2010 · 15,569 accessesView PDFCite
Research ArticleOpen accessIn the abstractLogistic Regression with Multi-Connected WeightsAvazjon Marakhimov Rakhimovich, Kabul Khudaybergenov Kadirbergenovich, Zahriddin Muminov Ishkobilovich, Kudaybergenov Jabbarbergen KadirbergenovichResults of current research in biological and cognitive science reveal that neuronal interactions predominantly depend on neurotransmitters for signaling and sending information between neurons. Furthermore, a specific neuron that is connected to the next neuron transmits or sends multiple neurotransmitters in parallel ways, where each neurotransmitter has specific functional roles. Based on these results, a new type of logistic regression … …Journal of Computer Science, Vol. 20, Issue 9 · 4 Jul 2024 · 5,059 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intelligent Approach to Develop, Assess and Optimize Energy Consumption Models for Air-Cooled Chillers using Machine Learning AlgorithmsMostafa Tahmasebi, Nabil NassifThe building sector accounts for more than 70% of the total electricity use. Chillers consume more than 50% of electrical energy during seasonal periods of building use. With the growth of the building sector and climate change, it's essential to develop energy-efficient HVAC systems that optimize the ever-increasing energy demand. This study aims to develop an energy consumption prediction model … …American Journal of Engineering and Applied Sciences, Vol. 15, Issue 3 · 8 Nov 2022 · 5,516 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredicting Depression Levels using Back Propagation Neural NetworkEddy Muntina Dharma, Yaya Heryadi, Lukas et al.Depression is a mood disorder characterized by feelings of deep sadness and a sense of indifference. When depression recurs in moderate or severe intensity, it can be a serious health condition. The most effective way to deal with this problem is to predict the symptoms of depression at an early stage. In this study, a Back Propagation Neural Network (BPNN) … …Journal of Computer Science, Vol. 18, Issue 3 · 18 Mar 2022 · 6,882 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimization of Multi-Layer Perceptron Deep Neural Networks using Genetic Algorithms for Hand Gesture RecognitionKhanh Nguyen-Trong, Thi-Thanh-Tan NguyenApplications of wearable sensors for Hand Gesture Recognition (HGR) have been gaining popularity in recent years. Among the proposed methods, deep neural networks with many hidden layers are promising to address the requirements of this wearable activity recognition. They can directly uncover features tied to the dynamics of HGR, from simple motion encoding in lower layers to more complex motion … …Journal of Computer Science, Vol. 18, Issue 2 · 8 Feb 2022 · 7,180 accessesView PDFCite
Research ArticleOpen accessIn the abstractEstimation of Soils Electrical Resistivity using Artificial Neural Network ApproachKpomonè Komla Apaloo-Bara, Adekunlé Akim Salami, Mawugno Koffi Kodjo et al.The knowledge of the ground electrical resistivity is essential to ensure the protection of electrical and telecommunications networks. However, the monitoring of its values is an expensive task which takes long time. Therefore, its prediction is important. This study investigates on predicting soil electrical resistivity using Artificial Neural Networks. Nine sites of our city (Lome, TOGO) were considered. After characterization … …American Journal of Applied Sciences, Vol. 16, Issue 2 · 13 Mar 2019 · 9,518 accessesView PDFCite
Research ArticleOpen accessIn the abstractCharacterization of the Changes in Opened Sufu Bottles During Storage with Mathematical ModelJingJing Liang, Dawei Li, Ruiqin Shi et al.Artificial Neural Network (ANN) is a type of nonlinear empirical model, which can clarify the complex relation between the inputs and outputs that allows it to approximate any nonlinear function for making predictions. The objective of this study is to monitor the Biogenic Amines (BAs) content and selected physicochemical properties of sufu (a traditional Chinese fermented soybean product) along time. … …American Journal of Biochemistry and Biotechnology, Vol. 14, Issue 4 · 12 Dec 2018 · 9,353 accessesView PDFCite
Research ArticleOpen accessIn the abstractA New Methodology for Neural Network Training Ensures Error Reduction in Time Series ForecastingPaola A. Sánchez-Sánchez, José Rafael García-GonzálezArtificial Neural Networks (ANN) consists of some components, such as architecture and learning algorithm. These components have a significant effect on the performance of the ANN, but finding good parameters is a difficult task to achieve. An important requirement for this task is to ensure the reduction of error when inputs and/or hidden neurons are added. In practice, it is … …Journal of Computer Science, Vol. 13, Issue 7 · 4 Jul 2017 · 7,171 accessesView PDFCite