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cellular neural/nonlinear networks

Cellular Neural Networks for NP-Hard Optimization

Cellular Neural Networks for NP-Hard Optimization

... A cellular neural/nonlinear network (CNN) is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a ...

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Matrix Measure Strategies for Stability of Cellular Neural Networks with Proportional Delays

Matrix Measure Strategies for Stability of Cellular Neural Networks with Proportional Delays

... of cellular neural networks with multiple proportional delays, Zheng et ...high-order networks with proportional ...of cellular neural networks with multi- proportional ...

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Extended LaSalle's Invariance Principle for Full-Range Cellular Neural Networks

Extended LaSalle's Invariance Principle for Full-Range Cellular Neural Networks

... In several applications for solving signal processing tasks in real time it is needed that a FR-CNN is convergent (or completely stable), that is, each solution is required to approach some equilibrium point in the ...

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Significant Location Detection & Prediction in Cellular Networks using Artificial Neural Networks

Significant Location Detection & Prediction in Cellular Networks using Artificial Neural Networks

... the nonlinear autoregressive neural network design available in the MATLAB software, as it is suitable for predicting a time series using past values by using supervised ...

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Brain Image Segmentation Using Multistable Cellular Neural Networks

Brain Image Segmentation Using Multistable Cellular Neural Networks

... piecewise nonlinear functions with more slopes instead of unit saturation function [10] which makes the multistable CNN capable of classifying more than two classes, In this study a polynomial function is ...

8

Training Artificial Neural Networks: Backpropagation via Nonlinear Optimization

Training Artificial Neural Networks: Backpropagation via Nonlinear Optimization

... In this paper we explore different strategies to guide backpropagation algorithm used for training artificial neural networks. Two different variants of steepest descent-based backpropagation algorithm, and ...

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Existence and Stability of Periodic Solution in Impulsive Hopfield Networks with Time-Varying Delays

Existence and Stability of Periodic Solution in Impulsive Hopfield Networks with Time-Varying Delays

... man-made neural systems, integration and communication delays are ubiquitous, and often become sources of instabil- ...electronic neural networks are usually time varying, and sometimes vary ...

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Integro differential inequality and stability of BAM FCNNs with time delays in the leakage terms and distributed delays

Integro differential inequality and stability of BAM FCNNs with time delays in the leakage terms and distributed delays

... In this paper, a class of impulsive bidirectional associative memory (BAM) fuzzy cellular neural networks (FCNNs) with time delays in the leakage terms and distributed delays is formulated and ...

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An Evolutionary Approach: Analysis of Artificial Neural Networks

An Evolutionary Approach: Analysis of Artificial Neural Networks

... artificial neural network solutions have been trained with ...artificial neural network must be trained before it becomes ...a neural network is compared to the desired ...

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Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE

Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE

... For supervised learning tasks, deep learning methods obviate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures which remove ...

5

An Introduction to Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

An Introduction to Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

... Neural networks are often used for statistical analysis and data modelling, in which their role is perceived as an alternative to standard nonlinear regression or cluster analysis techniques (Cheng ...

317

Smart Water: Short-Term Forecasting Application in Water Utilities

Smart Water: Short-Term Forecasting Application in Water Utilities

... and nonlinear autoregressive ANN models were employed to forecast water demand 24 hours ahead and 1 week ...of nonlinear ANN models compared to an existing, adequate linear ...

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Positioning Based on Factor Graphs

Positioning Based on Factor Graphs

... In this paper, we analyzed the mobile station positioning per- formance in wireless cellular networks using time di ff erence of arrival measurements in a new factor graphs framework. In this scenario, the ...

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THE ROLE OF INFORMATION TECHNOLOGY ON THE GROWTH OF FIRMS: A VALUE ADDED 
ONSIDERATION

THE ROLE OF INFORMATION TECHNOLOGY ON THE GROWTH OF FIRMS: A VALUE ADDED ONSIDERATION

... multilayer neural network with GA for selecting IDF and ontology based features gave 93% of accuracy, high precision and recall and lowest RMSE when comparing to all other ...

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Intrusion Detection System Using Hybrid Approach by MLP and K-Means Clustering

Intrusion Detection System Using Hybrid Approach by MLP and K-Means Clustering

... A more capable intrusion detection model is proposed by merging the skilled data mining techniques which consist of K-means clustering, Multilayer layer Perceptron (MLP) neural network and support vector machine ...

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DATA DRIVEN SOLUTIONS AND DISCOVERIES IN MECHANICS USING PHYSICS INFORMED NEURAL NETWORK

DATA DRIVEN SOLUTIONS AND DISCOVERIES IN MECHANICS USING PHYSICS INFORMED NEURAL NETWORK

... the neural networks [21], and to overcome this problem, we employ a non- dimensionalization technique with the purpose of scaling the input and the output of the neural network in proper scales ...

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Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

... a neural network? Figure 22 shows how a neural network can be embedded in the ...the neural network: the collision sensors to nodes in the collision layer, the proximity sensors to nodes in the ...

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Dynamical Nonlinear Neural Networks with Perturbations Modeling and Global Robust Stability Analysis

Dynamical Nonlinear Neural Networks with Perturbations Modeling and Global Robust Stability Analysis

... that neural networks are complex and large scale nonlinear dynamical ...the neural network field after publication of ...conceptualizing neural networks in terms of an energy ...

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Comparison of Different Neural Network Training Algorithms with Application to Face Recognition

Comparison of Different Neural Network Training Algorithms with Application to Face Recognition

... Artificial Neural Network (ANN) was used for further classification. ANN can be used for prediction for seen and unseen data. Different models of ANN have been used for face recognition, and reason for that is ...

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Global Asymptotical Stability of Delayed Impulsive Neural Networks without Lipschitz Neuron Activations

Global Asymptotical Stability of Delayed Impulsive Neural Networks without Lipschitz Neuron Activations

... delayed neural networks with impulses, where the neuron activations do not satisfy Lipschitz ...delayed neural networks with ...delayed neural networks with ...of neural ...

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