Spiking Neural Networks for the control of a servo system

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

doi:10.1109/icmech.2013.6518517

Cited by 7 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hardware configuration of the computer used is Intel Core I7 processor with 1 T Hz CPU, 4 GB RAM and the operating system used is Windows 7 Home Premium. The training algorithm is implemented on the RSNN to identify the nonlinear plant described by [5] (9)…”

Section: Simulation Resultsmentioning

confidence: 99%

“…(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%

See 2 more Smart Citations

Recurrent Spiking Neural Networks the Third Generation in Identification of Systems

Shiltagh¹

2014

IJCA

View full text Add to dashboard Cite

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. .

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Recurrent Spike Neural Network (Rsnn) Modelmentioning

confidence: 99%

See 1 more Smart Citation

Recurrent Spiking Neural Networks the Third Generation in Identification of Systems

Shiltagh¹

2014

IJCA

View full text Add to dashboard Cite

show abstract

“…The most accurate model among these models is HH, but it's very complex. SRM is simple due to its arithmetical simplicity and it becomes the most widely contributed model [12]. Therefore, SRM is used as a SNN model in this paper.…”

Section: A Neuron Modelmentioning

confidence: 99%

“…It is trained using SSO algorithm afterwards. The SRF termed the influence of the pre-synaptic neuron potential on the postsynaptic neuron potential [12]. There are different mathematical forms of SRF such as the hyperbolic tangent function that is used in this paper, as below [14]: tanh t τ (2) and its derivative:…”

Section: Fig1: Structure Of Snn: (A) Feed-forward Snn (B) Connectiomentioning

confidence: 99%

Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

Hassan¹,

Mahmood²

2019

IJCCCE

View full text Add to dashboard Cite

The design and simulation of the Spiking Neural Network (SNN) are proposed in this paper to control a plant without and with load. The proposed controller is performed using Spike Response Model. SNNs are more powerful than conventional artificial neural networks since they use fewer nodes to solve the same problem. The proposed controller is implemented using SNN to work with different structures as P, PI, PD or PID like to control linear and nonlinear models. This controller is designed in discrete form and has three inputs (error, integral of error and derivative of error) and has one output. The type of controller, number of hidden nodes, and number of synapses are set using external inputs. Sampling time is set according to the controlled model. Social-Spider Optimization algorithm is applied for learning the weights of the SNN layers. The proposed controller is tested with different linear and nonlinear models and different reference signals. Simulation results proved the efficiency of the suggested controller to reach accurate responses with minimum Mean Squared Error, small structure and minimum number of epochs under no load and load conditions.

show abstract

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

Al-Jamali

Al-Raweshidy

2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Spiking Neural Networks for the control of a servo system

Cited by 7 publications

References 15 publications

Recurrent Spiking Neural Networks the Third Generation in Identification of Systems

Recurrent Spiking Neural Networks the Third Generation in Identification of Systems

Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

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

Contact Info

Product

Resources

About