neural network inspired learning methods
A Dynamic Neural Network Architecture with immunology Inspired Optimization for Weather Data Forecasting
... and methods that can reveal insight about datasets that are complex, diverse, and of massive scale ...machine learning techniques including ANNscan play a crucial role in provide optimal solutions and ...
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A novel trio combo strategy for efficient team formation using hybrid triangulation mechanism
... machine learning approaches to form a triangular hybridization for a better quality team ...three methods inspired are Artificial Neural Network (ANN), Decision Tree (DT), and a ...
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A biologically inspired neural network controller for ballistic arm movements
... which neural signals, that is which forces, have to be gen- erated to reach a specific point in the ...the learning scheme modifies the neural features in order to map the working space and reach the ...
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Gist+RatSLAM: An Incremental Bio-inspired Place Recognition Front-End for RatSLAM
... self-organizing neural network is implemented, which models the competitive behavior of the cells found in visual and perirhinal ...place learning in unknown ...for learning large scale ...on ...
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A Technical Review on Intrusion Detection System
... Artificial neural networks (ANN) were developed as generalizations of mathematical models of biological nervous ...in neural network. The main processing elements of neural networks are called ...
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Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... of neural networks in literature re- search by Aston ...“the network can be taught to recognize individual writing styles, and the researchers used it to compare works attributed to Shakespeare and his ...
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Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator
... The flowchart of ANFIS procedure is shown in Figure 4. AN FIS distinguishes itself from normal fuzzy logic systems by the adaptive parameters, i.e., both the premise and consequent parameters are adjustable. The most ...
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A comparative performance evaluation of neural network based approach for sentiment classification of online reviews
... classification methods that have been so far proposed is dis- ...machine learning algorithms, SVM and naive bayes have been used widely for classification of online reviews (Pang et ...in neural ...
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Multi-step short-term power consumption forecasting with a hybrid deep learning strategy
... traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power ...
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Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements
... We would like to underline that we achieved much better results, namely 62% accuracy maximum ver- sus 83%, with Forex due to availability of more data that ca be used to implement a more effective learning ...
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Prediction of Heart Diseases on the basis of the Cleveland Database
... disease. Neural Networks are statistical tool inspired from biological neural network which can be used to perform various computational tasks such as pattern recognition, predictions, ...
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AN EFFICIENT SUPER PEER SELECTION ALGORITHM FOR PEER TO PEER (P2P) LIVE STREAMING NETWORK
... on Neural Network which didn’t represented before this time in this application and using PID controller to suppress the ...identification methods used to represent the models which are: ...
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Different Attack Patterns For Deep Brain Implants By Using Cnn
... neural network contains interconnected neurons used to commerce the messages from one ...making neural networks for ...problems. Neural networks are classified into 6 types such as (i) ...
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Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction
... On the other hand, forecasting the yield is not very easy as many controllable factors (e.g., applied irrigations, pest and fertilizer applications, etc.) and uncontrollable factors (e.g., weather, subsidies, and market, ...
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Modal Learning in a Neural Network
... computer network simulation environment whereby the system is able to discover alternative solutions in response to varying performance ...trainee network managers and a simulated network management ...
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Application Of Hybrid Model For Forecasting Prices Of Jasmine Flower In Bangalore, India
... the network correspond to the attributes measured for each training ...feed-forward network since none of the weight‘s cycles back to an input unit or to a previous layer‘s output ...
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Time Series Forecasting with Long Short-Term Memory Neural Networks on the Stock Market
... Machine learning is changing the ...main methods for a machine to learn: supervised and ...Supervised learning is done with a data set which is ...supervised learning are predicting the ...
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Neuro-Fuzzy Scheduler for the Control of Real Time Spherical Tank Process
... basic learning rule of adaptive network is back- propagation algorithm where the model parameters are updated by a gradient descent optimization ...hybrid learning algorithm, on the other hand is an ...
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OPTIMIZATION OF HIGH VOLTAGE POWER SUPPLY FOR INDUSTRIAL MICROWAVE GENERATORS FOR ONE MAGNETRON
... RBF neural network’s connection weight values, which not only can play the generalization ability of RBF neural network greatly, but also can improve the convergence speed and learning ability ...
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Brain-inspired replay for continual learning with artificial neural networks
... Efficiency and robustness of generative replay. These results highlight GR as a promising, perhaps unavoidable, tool for con- tinual learning in ANNs. However, although replaying generated data avoids the issue of ...
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