A Hybrid Higher Order Neural Classifier for handling classification problems

Fallahnezhad, Mehdi; Moradi, Mohammad Hassan; Zaferanlouei, Salman

doi:10.1016/j.eswa.2010.06.077

Cited by 50 publications

(12 citation statements)

References 34 publications

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“…Fallahnezhad et al [56] have proposed a novel hybrid higher order neural classifier which exhibits good generalization capabilities and improved accuracy for handling classification problems by taking into consideration a number of benchmark datasets. Yu et al [57] have used the gradient algorithm with synchronous update rule for training the ridge polynomial neural network for monotonicity and better convergence.…”

Section: Related Workmentioning

confidence: 99%

A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN) for nonlinear classification

Nayak

Naik

Behera

2015

Ain Shams Engineering Journal

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In this paper, a Chemical Reaction Optimization (CRO) based higher order neural network with a single hidden layer called Pi-Sigma Neural Network (PSNN) has been proposed for data classification which maintains fast learning capability and avoids the exponential increase of number of weights and processing units. CRO is a recent metaheuristic optimization algorithm inspired by chemical reactions, free from intricate operator and parameter settings such as other algorithms and loosely couples chemical reactions with optimization. The performance of the proposed CRO-PSNN has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN, PSO-PSNN. The methods have been implemented in MATLAB and the accuracy measures have been tested by using the ANOVA statistical tool. Experimental results show that the proposed method is fast, steady and reliable and provides better classification accuracy than others. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Related Workmentioning

confidence: 99%

A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN) for nonlinear classification

Nayak

Naik

Behera

2015

Ain Shams Engineering Journal

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“…In [11], a hybrid Taguchi-genetic algorithm is used to solve the problem of tuning both the network structure and parameters. In [12], a novel Hybrid Higher Order Neural Classifier (HHONC) based on HONN models is introduced, and then using the proposed classifier over various benchmark statistical datasets. A hybrid model Partial Least Square Neural Network (PLSNN) which combines PLS and NN is developed in [13] to enhance the detection performance, and also a Quantum-based Neural Network (QNN) is proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

Nuclear Reactors Safety Core Parameters Prediction using Artificial Neural Networks

Saber¹,

El-Koliel²,

El-Rashidy

et al. 2016

The International Conference on Electrical Engineering

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The present work investigates an appropriate algorithm based on Multilayer Perceptron Neural Network (MPNN), Apriori association rules and Particle Swarm Optimization (PSO) models for predicting two significant core safety parameters; the multiplication factor K eff and the power peaking factor P max of the benchmark 10 MW IAEA LEU research reactor. It provides a comprehensive analytic method for establishing an Artificial Neural Network (ANN) with selforganizing architecture by finding an optimal number of hidden layers and their neurons, a less number of effective features of data set and the most appropriate topology for internal connections. The performance of the proposed algorithm is evaluated using the 2-Dimensional neutronic diffusion code MUDICO-2D to obtain the data required for the training of the neural networks. Simulation results demonstrate the effectiveness and the notability of the proposed algorithm comparing with Trainlm-LM, quasi-Newton (Trainbfg-BFGS), and Resilient Propagation (trainrp-RPROP) algorithms.

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“…Varied structures of ANNs have been proposed to solve classification problems. They are basically used as supervised learning neural classifiers and unsupervised competitive learning classifiers (Fallahnezhad et al, 2011). In this research the authors have included some of the better known classifiers of ANN: multi-layer perceptron (MLP) (Hornik et al, 1989;Pérez-Miñana and Ross, 1996), radial basis function (RBF) (Park and Sandberg, 1991;Divsalar et al, 2011), support vector machines (SVM) (Vapnik, 1995;Tsai, 2008), recurrent neural networks (RNN) (Mandic and Chambers, 2001;Delgado et al, 2006), Jordan-Elman networks (ELN) (Jordan, 1986) and self-organizing feature maps (SOFM) (Kohonen, 1988;Burn and Home, 2008).…”

Section: Introductionmentioning

confidence: 99%

Towards a framework for multiple artificial neural network topologies validation by means of statistics

et al. 2012

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Artificial neural networks (ANNs) are flexible computing tools that have been applied to a wide range of domains with a notable level of accuracy. However, there are multiple choices of ANNs classifiers in the literature that produce dissimilar results. As a consequence of this, the selection of this classifier is crucial for the overall performance of the system. In this work, an integral framework is proposed for the optimization of different ANN classifiers based on statistical hypothesis testing. The framework is tested in a real ballistic scenario. The new quality measures introduced, based on the Student t‐test, and employed throughout the framework, ensure the validity of results from a statistical standpoint; they reduce the appearance of experimental errors or the appearance of possible randomness. Results show the relevance of this framework, proving that its application improves the performance and efficiency of multiple classifiers.

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A Hybrid Higher Order Neural Classifier for handling classification problems

Cited by 50 publications

References 34 publications

A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN) for nonlinear classification

A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN) for nonlinear classification

Nuclear Reactors Safety Core Parameters Prediction using Artificial Neural Networks

Towards a framework for multiple artificial neural network topologies validation by means of statistics

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