A theoretical framework for multiple neural network systems

Shields, Michael W.; Casey, Matthew

doi:10.1016/j.neucom.2007.05.008

Cited by 17 publications

(9 citation statements)

References 33 publications

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“…This sampling generates different partitioned parts. The data DS results of training datasets 1 , 2 , … have undergone the reordering algorithm in order to ensure sufficient training data with the diversity rule [12].…”

Section: Ensemble Learning Algorithm For Ann Clustermentioning

confidence: 99%

An Analysis of Large Data Classification using Ensemble Neural Network

Mohamad¹,

Nik²,

Zakaria³

et al. 2018

IJET

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In this paper, operational and complexity analysis are investigated for a proposed model of ensemble Artificial Neural Networks (ANN) multiple classifiers. The main idea to this is to employ more classifiers to obtain a more accurate prediction as well as to enhance the classification capabilities in case of larger data. The classification result analyzed between a single classifier and multiple classifiers followed by the estimates of upper bounds of converged functional error with the partitioning of the benchmark dataset. The estimates derived using the Apriori method shows that the proposed ensemble ANN algorithm with a different approach is feasible where such problems with a high number of inputs and classes can be solved with time complexity of O(n^k ) for some k, which is a type of polynomial. This result is in line with the significant performance achieved by the diversity rule applied with the use of reordering technique. As conclusion, an ensemble heterogeneous ANN classifier is practical and relevant to theoretical and experimental of combiners for the ensemble ANN classifier systems for a large dataset.

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Section: Ensemble Learning Algorithm For Ann Clustermentioning

confidence: 99%

An Analysis of Large Data Classification using Ensemble Neural Network

Mohamad¹,

Nik²,

Zakaria³

et al. 2018

IJET

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show abstract

“…In reality, human brain is a part of the central nervous system, it contains of the order of (10 +10 ) neurons. Each can activate in approximately 5ms and connects to the order of (10 +4 ) other neurons giving (10 +14 ) connections, (Shields & Casey, 2008). In reality, a typical neural net (with neurons) is shown in Fig.…”

Section: Artificial Neural Network Mapping: a Biological Inspirationmentioning

confidence: 99%

Robotics Arm Visual Servo: Estimation of Arm-Space Kinematics Relations with Epipolar Geometry

Mattar¹

2012

Robotic Systems - Applications, Control and Programming

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“…This led to the concept of "Multiple Neural Networks" systems for tackling complex tasks improving performances w.r.t. single network systems [2]. The idea is to decompose a large problem into a number of subproblems and then to combine the individual solutions to the subproblems into a solution to the original one [2].…”

Section: Introductionmentioning

confidence: 99%

“…single network systems [2]. The idea is to decompose a large problem into a number of subproblems and then to combine the individual solutions to the subproblems into a solution to the original one [2]. This modular approach can lead to systems in which the integration of expert modules can result in solving problems which otherwise would not have been possible using a single neural network [3].…”

Section: Introductionmentioning

confidence: 99%

A Continuous Learning in a Changing Environment

Dragoni

Vallesi

Baldassarri

2011

Image Analysis and Processing – ICIAP 2011

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Abstract. We propose a Hybrid System for dynamic environments, where a "Multiple Neural Networks" system works with Bayes Rule to solve a face recognition problem. One or more neural nets may no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. For this purpose, we assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net's degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used to establish who is the conflict winner, making the final choice (the name of subject). Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning.

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A theoretical framework for multiple neural network systems

Cited by 17 publications

References 33 publications

An Analysis of Large Data Classification using Ensemble Neural Network

An Analysis of Large Data Classification using Ensemble Neural Network

Robotics Arm Visual Servo: Estimation of Arm-Space Kinematics Relations with Epipolar Geometry

A Continuous Learning in a Changing Environment

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