A Regularizer Approach for RBF Networks Under the Concurrent Weight Failure Situation

Leung, Chi-Sing; Wan, Wai Yan; Feng, Ruibin

doi:10.1109/tnnls.2016.2536172

Cited by 37 publications

(17 citation statements)

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“…e reinforcement learning system also requires a strict policy so that the agent's behaviour can be regulated. It will also utilize reward function maps on each state-action pair of the environment to a numerical value which indicates the desirability of that state or the desirability of an action at that state [17,18].…”

Section: Ann Agents and Reinforcement Learningmentioning

confidence: 99%

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A Node-Regulated Deflection Routing Framework for Contention Minimization

Nleya

Mutsvangwa

2020

Journal of Computer Networks and Communications

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Optical Burst Switching (OBS) paradigm coupled with Dense Wavelength Division Multiplexing (DWDM) has become a practical candidate solution for the next-generation optical backbone networks. In its practical deployment only the edge nodes are provisioned with buffering capabilities, whereas all interior (core) nodes remain buffer-less. In that way the implementation becomes quite simple as well as cost effective as there will be no need for optical buffers in the interior. However, the buffer-less nature of the interior nodes makes such networks prone to data burst contention occurrences that lead to a degradation in overall network performance as a result of sporadic heavy burst losses. Such drawbacks can be partly countered by appropriately dimensioning available network resources and reactively by way of deflecting excess as well as contending data bursts to available least-cost alternate paths. However, the deflected data bursts (traffic) must not cause network performance degradations in the deflection routes. Because minimizing contention occurrences is key to provisioning a consistent Quality of Service (QoS), we therefore in this paper propose and analyze a framework (scheme) that seeks to intelligently deflect traffic in the core network such that QoS degradations caused by contention occurrences are minimized. This is by way of regulated deflection routing (rDr) in which neural network agents are utilized in reinforcing the deflection route choices at core nodes. The framework primarily relies on both reactive and proactive regulated deflection routing approaches in order to prevent or resolve data burst contentions. Simulation results show that the scheme does effectively improve overall network performance when compared with existing contention resolution approaches. Notably, the scheme minimizes burst losses, end-to-end delays, frequency of contention occurrences, and burst deflections.

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Section: Ann Agents and Reinforcement Learningmentioning

confidence: 99%

“…In (18), N drop xy is the total number of dropped bursts, while sent xy is the total number of successfully transmitted bursts on the given link.…”

Section: Reinforcement Learningmentioning

confidence: 99%

A Node-Regulated Deflection Routing Framework for Contention Minimization

Nleya

Mutsvangwa

2020

Journal of Computer Networks and Communications

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“…2) CONVERGENCE AND COMPLEXITY By substituting the optimal β n into (22), the change of the objective value n becomes Increment n by 1.…”

Section: A Node Fault Tolerant I-elm 1) Algorithmmentioning

confidence: 99%

“…However, these two incremental algorithms were designed for fault-free situations only. We believed that fault and noise could greatly degrade the performance for the I-ELM and CI-ELM, if special procedures were not considered [21], [22].…”

Section: Introductionmentioning

confidence: 99%

“…However, the objective function of the weight decay concept is not identical to the training set error of faulty networks. Hence, even for fault tolerant radial basis function (RBF) networks [22], [42], an optimal fault tolerant solution cannot be obtained, regardless of how the weight decay parameter is tuned. To the best of our knowledge, only few results in the fault tolerant incremental learning were reported [43].…”

Section: Introductionmentioning

confidence: 99%

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Fault and Noise Tolerance in the Incremental Extreme Learning Machine

Leung

Wong

2019

IEEE Access

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The extreme learning machine (ELM) is an efficient way to build single-hidden-layer feedforward networks (SLFNs). However, its fault tolerant ability is very weak. When node noise or node failure exist in a network trained by the ELM concept, the performance of the network is greatly degraded if a countermeasure is not taken. However, this kind of countermeasure for the ELM or incremental learning is seldom reported. This paper considers the situation that a trained SLFN suffers from the coexistence of node fault and node noise. We develop two fault tolerant incremental ELM algorithms for the regression problem, namely node fault tolerant incremental ELM (NFTI-ELM) and node fault tolerant convex incremental ELM (NFTCI-ELM). The NFTI-ELM determines the output weight of the newly inserted node only. We prove that in terms of the training set mean squared error (MSE) of faulty SLFNs, the NFTI-ELM converges. Our numerical results show that the NFTI-ELM is superior to the conventional ELM and incremental ELM algorithms under faulty situations. To further improve the performance, we propose the NFTCI-ELM algorithm. It not only determines the output weight of the newly inserted node, but also updates all previously trained output weights. In terms of training set MSE of faulty SLFNs, the NFTCI-ELM converges, and it is superior to the NFTI-ELM.INDEX TERMS Single hidden layer network, incremental learning, extreme learning machine, multiplicative noise, open fault.

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MCP Based Noise Resistant Algorithm for Training RBF Networks and Selecting Centers

Leung

Sum

2018

Neural Information Processing

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A Regularizer Approach for RBF Networks Under the Concurrent Weight Failure Situation

Cited by 37 publications

References 50 publications

A Node-Regulated Deflection Routing Framework for Contention Minimization

A Node-Regulated Deflection Routing Framework for Contention Minimization

Fault and Noise Tolerance in the Incremental Extreme Learning Machine

MCP Based Noise Resistant Algorithm for Training RBF Networks and Selecting Centers

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