The Random Neural Network: A Survey

Timotheou, Stelios

doi:10.1093/comjnl/bxp032

Cited by 89 publications

(79 citation statements)

References 82 publications

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“…An approach for using the model on Big Data contexts is presented in [5]. For more details about other applications of RNN, see [1,47].…”

Section: Random Neural Network [12]mentioning

confidence: 99%

Echo State Queueing Networks: A Combination of Reservoir Computing and Random Neural Networks

Basterrech¹,

Rubino²

2017

Prob. Eng. Inf. Sci.

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This paper deals with two ideas appeared during the last developing phase in Artificial Intelligence: Reservoir Computing (RC) and Random Neural Networks. Both have been very successful in many applications. We propose a new model belonging to the first class, taking the structure of the second for its dynamics. The new model is called Echo State Queuing Network. The paper positions the model in the global Machine Learning area, and provides examples of its use and performances. We show on largely used benchmarks that it is a very accurate tool, and we illustrate how it compares with standard RC models.

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“…An approach for using the model on Big Data contexts is presented in [5]. For more details about other applications of RNN, see [1,47].…”

Section: Random Neural Network [12]mentioning

confidence: 99%

Echo State Queueing Networks: A Combination of Reservoir Computing and Random Neural Networks

Basterrech¹,

Rubino²

2017

Prob. Eng. Inf. Sci.

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“…In contrast with existing AI/ML based approaches, RNNs inherent properties such as: (a) efficient computation (b) low complexity (c) energy-efficient hardware implementation (d) less dependency on network structure (e) strong generalization capability even with small training dataset, makes RNN a better choice for CE design [16]. Moreover, most of the presented RRM approaches in literature have failed to comply with LTEs full frequency spectrum usage requirement.…”

Section: Rrm and Icic Related Workmentioning

confidence: 99%

Impact of Learning Algorithms on Random Neural Network based Optimization for LTE-UL Systems

Adeel

Larijani

Javed

et al. 2015

NPA

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This paper presents a context-aware decision making solution to the problem of radio resource management (RRM) and inter-cell interference coordination (ICIC) in long-term evolution-uplink (LTE-UL) system. The limitations of existing analytical, artificial intelligence (AI), and machine learning (ML) based approaches are highlighted and a novel integration of random neural network (RNN) based learning with genetic algorithm (GA) based reasoning is presented. Initially, three learning algorithms (gradient descent (GD), adaptive inertia weight particle swarm optimization (AIW-PSO), and differential evolution (DE)) are applied to RNN and two learning algorithms (GD and levenberg-marquardt (LM)) are applied to artificial neural network (ANN). In the second phase, the GA based reasoning is applied to the trained ANN and RNN models for performance optimization. Finally, the ANN and RNN based optimization results are compared with the state-of-the-art fractional power control (FPC) schemes in terms of user throughput and power consumption. The simulation results revealed that an RNN-DE based cognitive engine (CE) can provide up to 14% more cell capacity along with 6dBm and 9dBm less user power consumption as compared to RNN-GD and FPC methods respectively.

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“…In the same context more advanced optimization algorithms have also been developed based on contrastive learning [43], quasi-Newton [38] and Levenberg-Marquardt [4] methods. A survey of RNN models, learning algorithms and applications can be found in [47].…”

Section: Introductionmentioning

confidence: 99%

Fast Non-Negative Least-Squares Learning in the Random Neural Network

Timotheou

2016

Prob. Eng. Inf. Sci.

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The random neural network is a biologically inspired neural model where neurons interact by probabilistically exchanging positive and negative unit-amplitude signals that has superior learning capabilities compared to other artificial neural networks. This paper considers non-negative least squares supervised learning in this context, and develops an approach that achieves fast execution and excellent learning capacity. This speedup is a result of significant enhancements in the solution of the non-negative least-squares problem which regard (a) the development of analytical expressions for the evaluation of the gradient and objective functions and (b) a novel limited-memory quasi-Newton solution algorithm. Simulation results in the context of optimizing the performance of a disaster management problem using supervised learning verify the efficiency of the approach, achieving two orders of magnitude execution speedup and improved solution quality compared to state-of-the-art algorithms.

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The Random Neural Network: A Survey

Cited by 89 publications

References 82 publications

Echo State Queueing Networks: A Combination of Reservoir Computing and Random Neural Networks

Echo State Queueing Networks: A Combination of Reservoir Computing and Random Neural Networks

Impact of Learning Algorithms on Random Neural Network based Optimization for LTE-UL Systems

Fast Non-Negative Least-Squares Learning in the Random Neural Network

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