Evolving Spiking Neural Networks for Nonlinear Control Problems

Qiu, Huanneng; Garratt, Matthew A.; Howard, David; Anavatti, Sreenatha G.

doi:10.1109/ssci.2018.8628848

Cited by 9 publications

(8 citation statements)

References 24 publications

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“…Fig. 7 shows the minimization of error during the training process as comparisons between the proposed structure and other structures proposed in [4] and [14]. It shows that MESNN can reach a lower error as compared with the Elman Neural Network and SNN.…”

Section: The Performance Of Identificationmentioning

confidence: 96%

“…The previous studies that we have compared with the present one relied in their proposals on different structures of neural networks. For example, in [4] the researchers adopted SNN with its training algorithm, while in the [14] the researchers used ENN with self feedback.…”

Section: The Performance Of Identificationmentioning

confidence: 99%

“…Hence, they are called spiking neural networks (SNNs) and from the taxonomy viewpoint, they belong to the third generation of NNs [3]. SNNs are strong computational modelling outfits that have attracted much attention, because of the effective bio-inspired modelling of synaptic interactions between neurons [4]. The basic Elman NN, which was introduced by [5] as one kind of partial recurrent NN model, is used for system identification and control.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: The Performance Of Identificationmentioning

confidence: 96%

Section: The Performance Of Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Al-Jamali

Al-Raweshidy

2020

IEEE Access

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“…Without explicit specific plant models, ESNN's most obvious advantage is that its neural networks can learn to carry out satisfactory tasks. In circumstances in which identical copies are hard to find, this is strongly favored [65] .…”

Section: Evolving Spiking Neural Networkmentioning

confidence: 99%

Hydrofluoroether Impurities—Chemical Detection Using a Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network

et al. 2020

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Hydrofluoroether (HFE) impurities detection is an issue related to detecting chemical contamination within a high volume manufacturing (HVM) chiller caused by a rapid emulated environmental attack. The aftereffects of the cycle emulated attack may eventually create a micro-crack in the heat-exchanger. This event eventually causes the contamination of HFE due to multiple chemical interactions. This study proposes a new classification methodology to detect HFE chemical impurities using induction by a 532nm laser. The purpose of the laser induction is to leverage its laser speckle contrast attributes and amplify its detection. One of the reasons for choosing the 532nm laser spectrum is its highest quantum efficiency. Once amplification of detection is achieved, the detector tends to be in a highly sensitive mode due to low marginality to differentiate between two different conditions. This mode is prone to be stochastic. Thus, a new form of architecture known as Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network (DL-LSC-ESNN) is proposed. The architecture utilizes speckle contrast domain conversion, dimensional additivity of the receptive neuron of evolving spiking neural network (ESNN), followed by the strength of Convolution Neural Network (CNN) feature extraction (FE) capability. Ultimately, the evolving and adaptive ability of ESNN is assimilated and integrated seamlessly. CNN acted to extract only an important spike train, and the result is its essences of important spikes or spike feature maps. The spike feature maps are then fed into the ESNN neuron repository, which either assimilates or creates a new neuron repository. The proposed methods show significant improvement in the accuracy of detection against multiple baseline state of the art CNN architecture and ultimately demonstrated its capability to detect real-time contamination of HFE thus improved the detection rate significantly for the HVM environment.

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“…In recent decades, SNNs have been of great interest in the computational intelligence community. Applications have been both non-behavioral [1] and behavioral [20,25]. Unlike traditional Artificial Neural Networks (ANNs) which carry out feed-forward computation based on weighted summation of real values, information transmission in SNNs is by means of discrete spikes generated during a potential integration process.…”

Section: Introductionmentioning

confidence: 99%

Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers

Qiu,

Garratt,

Howard

et al. 2020

Preprint

Self Cite

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A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality. This is especially the case for small Unmanned Aerial Vehicles (UAVs), as the platforms are highly dynamic and susceptible to breakage. Previous approaches often require simulation models with a high level of accuracy, otherwise significant errors may arise when the well-designed controller is being deployed onto the targeted platform. Here we try to overcome the transfer problem from a different perspective, by designing a spiking neurocontroller which uses synaptic plasticity to cross the reality gap via online adaptation. Through a set of experiments we show that the evolved plastic spiking controller can maintain its functionality by self-adapting to model changes that take place after evolutionary training, and consequently exhibit better performance than its non-plastic counterpart.

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Evolving Spiking Neural Networks for Nonlinear Control Problems

Cited by 9 publications

References 24 publications

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

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

Hydrofluoroether Impurities—Chemical Detection Using a Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network

Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers

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