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[PDF] Top 20 Improved spikeprop algorithm for neural network learning

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Improved spikeprop algorithm for neural network learning

Improved spikeprop algorithm for neural network learning

... Spiking Neural Networks and SpikeProp algorithm and the impact of error functions and learning parameters on BP and ...(SpikeProp algorithm) to improve performance (training and ... See full document

36

Method of Wireless Sensor Network Data Fusion

Method of Wireless Sensor Network Data Fusion

... and learning algorithm of RBF neural networks, a heterogeneous RBF neural network information fusion algo- rithm in wireless sensor networks is ...fusion algorithm. The proposed ... See full document

9

An Hybrid Approach for Enhancement of Energy and Network Life Time in WSN with PEGASIS and Artificial Neural Networks

An Hybrid Approach for Enhancement of Energy and Network Life Time in WSN with PEGASIS and Artificial Neural Networks

... an improved ant colony algorithm and neural network rather than the greedy algorithm to build the chain is ...of neural networks algorithm to select the chain head, it ... See full document

7

Support Vector Machine Neural Network Based Optimal Binary Classifier for Diabetic Retinopathy

Support Vector Machine Neural Network Based Optimal Binary Classifier for Diabetic Retinopathy

... Various neural network algorithms were used to develop diagnosis system such as Levenberg-Marquardt learning algorithm, classification tree analysis, Probabilistic Neural network ... See full document

6

Modular Neural Network Approach for Data Classification

Modular Neural Network Approach for Data Classification

... This learning algorithm [7] is applied to multilayer feed forward neural network [10] with different activation ...the network is trained it can produce its output very rapidly ... See full document

9

Neural Network Ensemble for the Prediction Of Pathological Complete Response After Neoadjuvant Chemotherapy for Breast Cancer

Neural Network Ensemble for the Prediction Of Pathological Complete Response After Neoadjuvant Chemotherapy for Breast Cancer

... suitable prognosis [3].In this research work, various machine learning algorithms are trained after pre-processing the data to check the performance of classification. The execution of the most prominent ... See full document

6

Improved PSO Algorithm for Training of Neural Network in Co design Architecture

Improved PSO Algorithm for Training of Neural Network in Co design Architecture

... In the previous research, a new architecture for co-design between hardware and software was proposed. This architecture had not only the advantages of the software side but also the advantage of the hardware side. In ... See full document

7

Research on neural network chaotic encryption algorithm in wireless network security communication

Research on neural network chaotic encryption algorithm in wireless network security communication

... resentative algorithm of the corresponding asymmetric algorithm is RSA algorithm ...the algorithm is that the length of the key has reached 56 bits, which cannot meet the current wireless ... See full document

10

Integration Intelligent Estimators to Disturbance Observer to Enhance Robustness of Active Magnetic Bearing Controller

Integration Intelligent Estimators to Disturbance Observer to Enhance Robustness of Active Magnetic Bearing Controller

... Observer Algorithm (DO) is an approach to reduce effects of ...artificial neural network and iterative learning were hybridized to DO as intelligent techniques in estimation ...iterative ... See full document

7

Model of Electric Power Load by Adaptive Neural Network

Model of Electric Power Load by Adaptive Neural Network

... Backpropagation algorithm has become a common algorithm used for training feed-forward multilayer ...Square algorithm that minimizes the mean square error between the target output and the ... See full document

6

Bacterial foraging optmization algorithm for neural network learning  enhancement

Bacterial foraging optmization algorithm for neural network learning enhancement

... the network on learning from its surroundings and improves the performance of this model during the process of learning (Haza, ...2006). Learning is an operation of the optimization of the ... See full document

32

(EM) mechanism, for global

(EM) mechanism, for global

... electromagnetism-like algorithm (EM), we propose a novel hybrid learning algorithms which is the improved EM algorithm with genetic algorithm technique (IEMGA) for recurrent fuzzy ... See full document

6

An Improved Collaborative Algorithm with Artificial Neural Network in Multidisciplinary Design Optimization of AUV

An Improved Collaborative Algorithm with Artificial Neural Network in Multidisciplinary Design Optimization of AUV

... the improved multidisciplinary CO (IMCO) framework, the integrated MDO problem of low resistance, low noise and high maneuvering performance of AUV shape is decomposed into: a system level and three subsystem ... See full document

10

An Indoor Localization Algorithm Based on RBF Neural Network Optimized by the Improved PSO

An Indoor Localization Algorithm Based on RBF Neural Network Optimized by the Improved PSO

... Combined with the RAN, the number of nodes in hidden layer increase dynamically to determine the center, the number of nodes in hidden layer and spread constant of RBFNN. The inertia weight of PSO is improved to ... See full document

8

PID Neural Network Motor Synchronization Control Based on the Improved PSO Algorithm

PID Neural Network Motor Synchronization Control Based on the Improved PSO Algorithm

... optimization algorithm, and blends the mutation of genetic algorithm into the particle swarm, that is to initialize some variables again at a certain ...of algorithm to find the best ... See full document

11

Algorithm animation and its application to artificial neural network learning

Algorithm animation and its application to artificial neural network learning

... is a global symbol defined previously in the user's login To DCL, "netviz," command file: :== netviz 1.2 Startup NetViz set up and $ : netviz set/ lflag 1 set/ mode/ learn_grain 500 nep[r] ... See full document

230

Comparative Analysis of Classification Algorithms on Different Datasets using WEKA

Comparative Analysis of Classification Algorithms on Different Datasets using WEKA

... of neural networks in classification is not uncommon in machine learning community ...cases, neural networks give a lower classification error rate than the decision trees but require longer ... See full document

5

Deep Machine Learning In Neural Networks

Deep Machine Learning In Neural Networks

... deep neural networks (DNNs) have the demand on quality ...the neural network framework. The optimization, network pruning and error bound assessments are done by error bound configuration, for ... See full document

8

USE OF GENETIC SVM FOR ECG ARRHYTHMIA CLASSIFICATION

USE OF GENETIC SVM FOR ECG ARRHYTHMIA CLASSIFICATION

... e Neural Network Training: The training to the network is given using a Supervised learning algorithm;back- propogation This algorithm looks for the minimum of the error function ... See full document

9

A novel improved extreme learning machine algorithm in solving ordinary differential equations by Legendre neural network methods

A novel improved extreme learning machine algorithm in solving ordinary differential equations by Legendre neural network methods

... the neural network model; Lagaris and Likas [23] pro- posed that multi-layer perceptrons can be used to construct approximate solutions; a hy- brid technique for constructing the neural ... See full document

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