C-NNAP: a dedicated platform for binary neural networks

Kennedy, J.V.; Austin, Jim; Pack, Robert C.; Cass, B.

doi:10.1049/cp:19970720

Cited by 6 publications

(6 citation statements)

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“…Particle Swarm Optimization (PSO) is an evolutionary computational technique invented by Dr Eberhart and Dr Kennedy. 26 The PSO algorithm converges quickly and efficiently, but is prone to fall into local optima. The Beetle Antennae search algorithm (BAS) 27 performs optimization calculations by simulating the process of aspens searching for food, which is less computational and less complex, and has advantages in local search.…”

Section: Ies Operation Optimization Modelmentioning

confidence: 99%

Integrated energy system operation considering building thermal inertia and hydrogen storage systems

Wang¹,

Qin²,

Wang³

et al. 2023

Sustainable Energy Fuels

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Section: Ies Operation Optimization Modelmentioning

confidence: 99%

Integrated energy system operation considering building thermal inertia and hydrogen storage systems

Wang¹,

Qin²,

Wang³

et al. 2023

Sustainable Energy Fuels

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“…Particle Swarm Optimization. The particle swarm optimization (PSO) is inspired on observations of social and collective behavior as well as bird flocking or the model human social behavior [5]. In the particle matters, one particle represents a potential solution.…”

Section: Swarm Intelligence Algorithmsmentioning

confidence: 99%

A New Automatic Modulation Classifier Using Swarm Intelligence-Trained Neural Network

Cheng

Liu

2013

AMR

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Monitoring modulation type of the detected signal is the most important intermediate step between signal detection and demodulation. The back propagation neural network (BPNN) was widely used in constructing modulated signal classifier in the field of automatic modulation classification (AMC). There are many visible features in the back propagation (BP) algorithm including adaptive learning, the ability of fault tolerant, etc. However, this algorithm has two main disadvantages, such as the slow convergence speed and easily falling into the local minimum. This paper presents a novel modulation classifier using BPNN trained with swarm intelligence algorithms (SIA), for the sake of overcoming these deficiencies. The initial weights and thresholds of BP neural network were optimized by SIA. As the SIA has an excellent global search property, this classifier can consume less training time and improve the automatic modulation type identification rate.

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“…The PSO is an evolutionary algorithm first proposed by Kennedy and Eberhart in 1995, which is on the study of the feeding behavior of bird and fish [8]. The swarm can find the best position through interaction of each other.…”

Section: Algorithm Of Psomentioning

confidence: 99%

Fault Diagnosis of PT Fuel System Based on Particle Swarm Optimization-Support Vector Machine

Wang

et al. 2011

AMM

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In order to overcome the difficulty in selecting parameters of support vector machine (SVM) when modeling the PT fuel system fault diagnosis, SVM optimized by particle swarm optimization (PSO) algorithm was proposed. The PSO-SVM model was established and the fault multi-classifiers of the SVM were got. The pressure signal of the PT fuel inlet and outlet at different rotational speed and conditions was collected. The algorithm of PSO-SVM was used to train and recognize the pressure signal. The result of experiment confirms the validity of this method through comparison of the BP-NN, SVM and the PSO-SVM.

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C-NNAP: a dedicated platform for binary neural networks

Cited by 6 publications

References 0 publications

Integrated energy system operation considering building thermal inertia and hydrogen storage systems

Integrated energy system operation considering building thermal inertia and hydrogen storage systems

A New Automatic Modulation Classifier Using Swarm Intelligence-Trained Neural Network

Fault Diagnosis of PT Fuel System Based on Particle Swarm Optimization-Support Vector Machine

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