Competitive probabilistic neural network

Zeinali, Yasha; Story, Brett

doi:10.3233/ica-170540

Cited by 66 publications

(40 citation statements)

References 63 publications

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“…The large number of published articles on MI–BCI tasks using EEG signals highlights the importance of the applicability of EEG signals in the BCI domain (Hwang et al, ; Ortiz‐Rosario and Adeli, ). Most EEG‐based BCI systems usually use a structured approach that includes three main steps: (a) preprocessing that may contain three suboperations of noise removal (NR; Çınar, S., and Acır, ; Mutanen et al, ), channel selection (CS; Ghaemi et al, ; Rathee et al, ), and data augmentation (Kalunga et al, 2015; Krell et al, 2018); (b) feature construction, that is choosing appropriate properties of signals, consists of two suboperations of feature extraction (Hsu, ; Zhang et al, ; Aghaei et al, ; Cai et al, ), and feature selection (Zhang et al, ; Lin et al, ; Ma et al, ); and (c) classification that is performed using an appropriate classifier such as support vector machine (Khedher et al, ; Dai and Cao, ; Direito et al, ), probabilistic neural networks (Adeli and Panakkat, ; Sankari and Adeli, ), enhanced probabilistic neural network (Ahmadlou and Adeli, ; Hirschauer et al, ; Fernandes et al, ), competitive probabilistic neural network (Zeinali and Story, ), the recently developed neural dynamics classification algorithm (Rafiei and Adeli, ), or a combination or ensemble of classifiers (Oliveira‐Santos et al ; Reyes et al ). It should be noted that the necessity of each aforementioned suboperation is usually determined by a BCI expert, which is not convenient in practice.…”

Section: Introductionmentioning

confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.

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Section: Introductionmentioning

confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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“…Deep neural networks have been successfully applied in a variety of civil engineering applications. Examples include structural damage detection and image recognition (Cha et al., ; Lin et al., ; Zhang et al., ; Koziarski and Cyganek, ; Zeinali and Story, ), estimation of concrete compressive strength (Rafiei et al., ), real‐state sale price estimation (Rafiei and Adeli, ), and development of a novel earthquake early warning system (Rafiei and Adeli, ).…”

Section: Surrogate Model For Two‐terminal Connectivitymentioning

confidence: 99%

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

Nabian

Meidani

2018

Computer aided Civil Eng

122

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To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating seismic reliability analysis, on a transportation network case study. Two distinct deep neural network surrogates are constructed and studied: (1) a classifier surrogate that speeds up the connectivity determination of networks and (2) an end‐to‐end surrogate that replaces modules such as roadway status realization, connectivity determination, and connectivity averaging. Numerical results from k‐terminal connectivity analysis of a California transportation network subject to a probabilistic earthquake event demonstrate the effectiveness of the proposed surrogates in accelerating reliability analysis while achieving accuracies of at least 99%.

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“…and others, which has proved to be of great practical value in engineering management (Wang, Xu, Wang, You, & Tan, ). Though a single algorithm has some advantages such as low complexity and high efficiency (Montalvo, Izquierdo, Pérez‐García, & Herrera, ), the commonly used data mining algorithms such as support vector machines (Dai, & Cao, ), neural networks (Rigos, Tsekouras, Vousdoukas, Chatzipavlis, & Velegrakis, ; Zeinalia & Story, ), and decision trees have initial parameters in these methods that are not easy to adjust (Hong, Dong, Chen, & Wei, ; Üstün, Melssen, Oudenhuijzen, & Buydens, ; Yang, Deb, & Fong, ), and the prediction accuracy of a single model is often not sufficient for the solution of ever increasingly complicated pattern recognition problems (Chou, Cheng, & Wu, ; Van Wezel & Potharst, ). In a seminal book, Adeli and Hung () demonstrated how integration of the three main computational intelligence (CI) techniques of neural networks, fuzzy logic, and genetic algorithms can solve the complicated face recognition and engineering design problems more effectively than any of those approaches used alone.…”

Section: Related Workmentioning

confidence: 99%

“…Vousdoukas, Chatzipavlis, & Velegrakis, 2016;Zeinalia & Story, 2017), and decision trees have initial parameters in these methods that are not easy to adjust (Hong, Dong, Chen, & Wei, 2011;Üstün, Melssen, Oudenhuijzen, & Buydens, 2005;Yang, Deb, & Fong, 2011), and the prediction accuracy of a single model is often not sufficient for the solution of ever increasingly complicated pattern recognition problems (Chou, Cheng, & Wu, 2013;Van Wezel & Potharst, 2007). In a seminal book, Adeli and Hung (1994) demonstrated how integration of the three main computational intelligence (CI) techniques of neural networks, fuzzy logic, and genetic algorithms can solve the complicated face recognition and engineering design problems more effectively than any of those approaches used alone.…”

mentioning

confidence: 99%

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

et al. 2018

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Compaction quality assessment and control for an earth‐rock dam is the key measure to ensure dam safety. However, to date, the compaction quality assessment model has not been accurate enough, and no effective feedback control measures have been developed. Hybrid data mining algorithms have great potential for solving this problem. In this study, smart bacteria‐foraging algorithm‐based customized kernel support vector regression (SBFA‐CKSVR) is proposed for compaction quality assessment, whereas an enhanced probabilistic neural network (EPNN) is adopted for compaction quality control. SBFA integrates a bacteria‐foraging algorithm, chaos mapping, and adaptive and quantum computing to solve the high‐dimensional complex problem effectively. CKSVR is proposed to approximate a function in quadratic continuous integral space L2(R) where its hyperparameters are optimized by SBFA. Finally, SBFA‐CKSVR is used to establish a high‐precision compaction quality assessment model whereas the EPNN is adopted to realize the compaction quality feedback control. A three‐dimensional real‐time monitoring system for the earth‐rock dam is also developed based on SBFA‐CKSVR and EPNN. A large‐scale hydraulic engineering application proves the effectiveness and superiority of this research compared with the previous work.

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Competitive probabilistic neural network

Cited by 66 publications

References 63 publications

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam

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