The Parallel Approach to the Conjugate Gradient Learning Algorithm for the Feedforward Neural Networks

Bilski, Jarosław; Smoląg, Jacek; Galushkin, A.I.

doi:10.1007/978-3-319-07173-2_2

Cited by 29 publications

(4 citation statements)

References 18 publications

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“…Those event instances can be mapped to a multilayer feed forward neural network with conjugate gradient back propagation algorithm as training algorithm [33]. In this algorithm, the weights of entire network may be considered as single input vector and their derivatives constitute the gradient vector [34].…”

Section: Knowledge Exploration the Knowledge Organization Frommentioning

confidence: 99%

A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

Mishra

Lin

Chang

2015

International Journal of Distributed Sensor Networks

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In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propose a Cognitive Oriented IoT Big-data Framework (COIB-framework) along with implementation architecture, IoT big-data layering architecture, and data organization and knowledge exploration subsystem for effective data management and knowledge discovery that is well-suited with the large scale industrial automation applications. The discussion and analysis show that the proposed framework and architectures create a reasonable solution in implementing IoT big-data based smart industrial applications.

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Section: Knowledge Exploration the Knowledge Organization Frommentioning

confidence: 99%

A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

Mishra

Lin

Chang

2015

International Journal of Distributed Sensor Networks

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“…5. The A processing elements are used to calculate the error ε (9), and the D processing elements compute errors in the hidden layers (8). The E processing elements determine the elements of the Jacobian matrix (10).…”

Section: Parallel Realisationmentioning

confidence: 99%

Parallel Approach to the Levenberg-Marquardt Learning Algorithm for Feedforward Neural Networks

Bilski

Smoląg

Żurada

2015

Artificial Intelligence and Soft Computing

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“…[6,61]) and the learning properties of neural networks (see e.g. [7,8,9,10,11,12,13,14,15,51,94,98]). In particular we propose: (a) a new approach for construction and tuning of neuro-fuzzy systems (including flexible neurofuzzy systems, see e.g.…”

Section: Introductionmentioning

confidence: 99%

A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

Łapa

Cpałka

Galushkin

2015

Artificial Intelligence and Soft Computing

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Abstract. In this paper a new approach for construction of neuro-fuzzy systems for nonlinear classification is introduced. In particular, we concentrate on the flexible neuro-fuzzy systems which allow us to extend notation of rules with weights of fuzzy sets. The proposed approach uses possibilities of hybrid evolutionary algorithm and interpretability criteria of expert knowledge. These criteria include not only complexity of the system, but also semantics of the rules. The approach presented in our paper was tested on typical nonlinear classification simulation problems.

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The Parallel Approach to the Conjugate Gradient Learning Algorithm for the Feedforward Neural Networks

Cited by 29 publications

References 18 publications

A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

Parallel Approach to the Levenberg-Marquardt Learning Algorithm for Feedforward Neural Networks

A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

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