[PDF] Top 20 Neural Network Performance for Complex Minimization Problem
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Neural Network Performance for Complex Minimization Problem
... Artificial Neural Network, as it is shown in [5] in the case of the hard shower component registered by the experiment KASCADE [4], can be quite ...similar network architec- ture which schematic view ... See full document
7
Radio location of partial discharge sources : a support vector regression approach
... a network of inexpensive radio sensors to sample the spatial patterns of PD received signal ...Their performance is compared to that of artificial neural network (ANN) ... See full document
9
Combined Complex-Valued Artificial Neural Network (CCVANN)
... single, neural network ...for neural network models (SNN) that consist of a combination of the candidate neural ...combining neural network models in ...improved ... See full document
5
Robust beamforming techniques for non-orthogonal multiple access systems with bounded channel uncertanties
... the performance of the SIC based receivers as the decoding order of the received multi- user signals is determined with respect to the users’ effective channel ...case performance optimization framework to ... See full document
6
Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm
... a complex nonlinear relationship to a great ...artificial neural network is widely used in solving complex nonlinear problems, the researchers also proposed some neural network ... See full document
6
Economic Approach for Stochastic Artificial insemination by Neural Network
... common neural network model is the multi-layer perceptron ...of neural network is known as a supervised network because it requires a desired output in order to ...of network is ... See full document
5
THE OPTIMAL PERFORMANCE OF MULTI-LAYER NEURAL NETWORK FOR SPEAKER-INDEPENDENT ISOLATED SPOKEN MALAY PARLIAMENTARY SPEECH
... The pre-processing block designed in speech recognition aims towards reducing the complexity of the problem before the next stage start to work with the data. As mentioned above, the digitized speeches will be ... See full document
14
Performance Evaluation of Decision Making Units Using Data Envelopment Model and Artificial Neural Network (Case Study: Fars Regional Water Corporation)
... The performance limit obtained by the DEA is also sensitive to statistical perturbations and outliers caused by measurement error or any other external factor, causing the efficiency limit to be shifted and ... See full document
18
Multi Layer Feed Forward Neural Network Knowledge Base to Future Stock Market Prediction
... Knowledge is a communication progression between the learner and the surroundings. The learner receives information from the surroundings, and processes the information to form knowledge. The learner uses this learned ... See full document
15
Neural Network based Minimization of BER in Multi-User Detection in SDMA
... Neural networks can be used for linear design, adaptive prediction, amplitude detection, character recognition and many other applications. This paper investigates the use of neural network based ... See full document
7
Research of Teacher’s Performance Evaluation Model Based on AHP and Improved PSO BP Neural Network
... BP neural network is suitable for the comprehensive evaluation and modeling of University teachers' performance with non-linear characteristics, but it has some ... See full document
6
A modified ant colony optimization algorithm for network coding resource minimization
... As mentioned above, BLS and BTS encodings both record the explicit link states (active or inactive). But, the third category of the EAs utilizes the relative information of the flows [35]. To be specific, each link is ... See full document
18
Optimization in an Error Backpropagation Neural Network Environment with a Performance Test on a Pattern Classification Problem
... the network training problem is described as a problem of minimizing the multiple class cross- entropy error ...classication problem in which the classier is trained with examples of the ... See full document
29
Remarks on Direct System Identification Using Hypercomplex–Valued Neural Network with Application to Time–Series Estimation
... for neural networks has attracted increasing attention from engineering re- search fields because it helps to learn to handle a wide variety of geometric objects and their transformation in the form of ... See full document
5
Research on road extraction of remote sensing image based on convolutional neural network
... road network information plays a very important role in traffic management, urban plan- ning, automatic vehicle navigation, and emergency man- agement ...road network information in time to achieve dynamic ... See full document
11
Using Feature Weights to Improve Performance of Neural Networks
... improve performance of inductive machine learning ...improves performance considerably [Guyon & Elisseeff, ...learning problem, so totally discarding them may impede accuracy; This is the reason ... See full document
6
Concurrent blind channel equalization with phase transmittance RBF neural networks
... proposed complex valued radial basis function (RBF) neural network with phase transmittance (PTRBFNN) outperforms the classical complex valued RBF neural network (RBFNN) for ... See full document
8
Performance Evaluation of Neural Network and Deep Neural Network for Human Activity Recognition
... more complex feature set to classify complicated activities [17] ...human-level performance [21] ...22 complex in- home activities ...whole network got higher accuracy than traditional methods ... See full document
7
Rethinking Complex Neural Network Architectures for Document Classification
... clarify. Complex architectures are more difficult to train, more sensitive to hyperparameters, and brittle with respect to domains with different data characteristics—thus both exacerbating the “crisis of ... See full document
6
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 ...the ... See full document
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