Spiking neural networks for identification and control of dynamic plants

Abiyev, Rahib H.; Kaynak, Okyay; Oniz, Yesim

doi:10.1109/aim.2012.6265983

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…SNN mimics biological nervous system more closely compared to conventional artificial neural networks [14]. Although SNN is biologically more realistic than artificial neural network (ANN) but receives less attention than ANN due to the difficulty to train SNN [15]. In order to overcome the non-differentiability of spike function that leads to difficulty in SNN training, deep reinforcement learning is applied to balance the firing rate of excitatory and inhibitory population of spike neuron.…”

Section: Issn: 2252-8938mentioning

confidence: 99%

Spike neuron optimization using deep reinforcement learning

Hui

Ishak

2021

IJ-AI

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Deep reinforcement learning (DRL) which involved reinforcement learning and artificial neural network allows agents to take the best possible actions to achieve goals. Spiking Neural Network (SNN) faced difficulty in training due to the non-differentiable spike function of spike neuron. In order to overcome the difficulty, Deep Q network (DQN) and Deep Q learning with normalized advantage function (NAF) are proposed to interact with a custom environment. DQN is applied for discrete action space whereas NAF is implemented for continuous action space. The model is trained and tested to validate its performance in order to balance the firing rate of excitatory and inhibitory population of spike neuron by using both algorithms. Training results showed both agents able to explore in the custom environment with OpenAI Gym framework. The trained model for both algorithms capable to balance the firing rate of excitatory and inhibitory of the spike neuron. NAF achieved 0.80% of the average percentage error of rate of difference between target and actual neuron rate whereas DQN obtained 0.96%. NAF attained the goal faster than DQN with only 3 steps taken for actual output neuron rate to meet with or close to target neuron firing rate.

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Section: Issn: 2252-8938mentioning

confidence: 99%

Spike neuron optimization using deep reinforcement learning

Hui

Ishak

2021

IJ-AI

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“…The computational power of SNNs outstrips that of classical neural networks that use threshold or sigmoidal activation functions. Furthermore, SNNs have the potential for quick adaptation [17]- [23]. Given these advantages, as above mentioned, an SNN with an Elman NN is considered in this paper for the identification and control of dynamic plants.…”

Section: Introductionmentioning

confidence: 99%

Modified Elman Spike Neural Network for Identification and Control of Dynamic System

Al-Jamali

Al-Raweshidy

2020

IEEE Access

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The utilization of conventional modeling strategies in the identification and control of a nonlinear dynamical system suffers from some weaknesses. These include absence of precise, conventional knowledge about the system, a high degree of uncertainty, strongly nonlinear and time-varying behavior. In this paper, a modified training algorithm for the identification and control of a nonlinear system using a soft-computing approach is proposed. Specifically, a modified structure of the Elman neural network with spike neural networks is proposed. This modified structure includes self-feedback, which provides a dynamic trace of the training algorithm. This self-feedback has weights, which can be trained during the training process. The simulation results show that the modified structure with the modified training algorithm is capable of the identification and control of a dynamic system in a more robust manor than when solely applying the other types of neural networks by 70% in terms of minimization of the percentage of error. INDEX TERMS Identification, dynamic system, modified Elman spike neural network, spike neural network.

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“…(1) each synapse has its self-delay which is different from the delay of the other synapse, The neuron generates presynaptic single spike that increases or decreases the membrane potential. If the weighted sum of the incoming postsynaptic potentials generated by presynaptic neurons reaches a threshold value ( ), then the neuron fires [4,5]. …”

Section: Recurrent Spike Neural Network (Rsnn) Modelmentioning

confidence: 99%

“…(4) (4) Where are the actual firing time and desired spike time respectively then the derivatives of eq. (1) is (5) The weights are adaptive based on error the propagated from output to hidden to input layer, this can be represented according to [4,5] (6)…”

Section: The Training Algorithmmentioning

confidence: 99%

Recurrent Spiking Neural Networks the Third Generation in Identification of Systems

Shiltagh¹

2014

IJCA

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In this paper the modified identification method for nonlinear systems is proposed based on Recurrent Spiking Neural Networks (RSNN). Spike Response Model (SRM) has been employed in the modification method. The learning of the parameters of RSNN is based on modified backpropagation algorithm which is known as SpikeProp. In the identification of a variety of types of nonlinear systems, a coding equation is applied to convert real numbers into spike times. The RSNN structure is tested for the identification of the nonlinear systems. The simulation results show that the proposed modification method provides a good performance in terms of execution time and minimizing error in the training phase. .

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Spiking neural networks for identification and control of dynamic plants

Cited by 12 publications

References 29 publications

Spike neuron optimization using deep reinforcement learning

Spike neuron optimization using deep reinforcement learning

Modified Elman Spike Neural Network for Identification and Control of Dynamic System

Recurrent Spiking Neural Networks the Third Generation in Identification of Systems

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