Spiking neural network-based control chart pattern recognition

Awadalla, Medhat; Sadek, Mohamed

doi:10.1016/j.aej.2012.07.004

Cited by 27 publications

(14 citation statements)

References 8 publications

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“…The relationship between the spikes from the input and the variables of internal state have been described with the spiking neurons. These spiking neurons were adopted on the basis of the Spike Response Model or SRM [20]. In this approach, a neuron j is considered with a set of immediate pre-synaptic neurons known as D j .…”

Section: Spiking Neural Network Architecturementioning

confidence: 99%

“…The work has been performed on the supervised learning [20] that aimed to improve the supervised learning algorithm known as the SpikeProp, which looks like the traditional back propagation error algorithm. The algorithm objective is to learn a group of target firing times that occurs at the output neurons for a provided set of input patterns.…”

Section: A Spiking Neural Network For Supervised Learning Proceduresmentioning

confidence: 99%

“…The architectural difference is attributed to the synaptic terminals located between each layer of neurons and delay in synapses. The behavior of spiking neurons has been described by many proposed mathematical models [20][21]. The model is composed of a spiking neural network in connection with a feed forward that delivers multitude synaptic terminals with delay.…”

Section: Spiking Neural Network Architecturementioning

confidence: 99%

See 2 more Smart Citations

Spiking Neural Network and Bull Genetic Algorithm for Active Vibration Control

Awadalla¹,

Computers²

2018

IJISA

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Abstract-Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2% performance improvement over spiking neural network.

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Section: Spiking Neural Network Architecturementioning

confidence: 99%

Section: A Spiking Neural Network For Supervised Learning Proceduresmentioning

confidence: 99%

Section: Spiking Neural Network Architecturementioning

confidence: 99%

See 1 more Smart Citation

Spiking Neural Network and Bull Genetic Algorithm for Active Vibration Control

Awadalla¹,

Computers²

2018

IJISA

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“…They apply various classification methods. In [11] spiking neural networks are used. The paper [12] presents a system for recognizing a large class of engineering drawings characterized by alternating instances of symbols and connection lines.…”

Section: Related Workmentioning

confidence: 99%

Logical Structure Recognition of Diagram Images

Sas

Markowska–Kaczmar

2015

Annals of Computer Science and Information Systems

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Abstract-This document presents a method of a logical links structure recognition between elements on diagrams. The applied approach intuitively mimics a human way of recognition that relies on merging already found connectors into more complex ones. This procedure is modeled by our method where simple and obvious connectors and gradually extended to more complex structures. Each iteration may lead to modification of connectors set obtained so far. The modifications are managed by a rules set describing logical and graphical constraints that should be satisfied by the connectors structure. If the extension leads to violation of constraints defined by the rules then the modification is not carried out. In this way, the recognized diagram structure is consistent with the assumed principles. The method was experimentally validated using the set of diagrams from three domains. In conclusions, method's advantages and drawbacks are discussed.

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“…Networks with spiking neurons play an important role in many applications of pattern recognition (Awadalla & Sadek, 2012) and computational algorithms (Bower, 2013;Brody & Hopfield, 2003). Neural populations demonstrate wide diapason of their flexible feedback for changes in the input stimuli.…”

Section: Introductionmentioning

confidence: 99%