Fractional-Order Deep Backpropagation Neural Network

Bao, Chunhui; Pu, YoungGun; Zhang, Yi

doi:10.1155/2018/7361628

Cited by 49 publications

(29 citation statements)

References 37 publications

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“…Bao in his research proposed a fractional-order deep backpropagation (BP) neural network model with Caputo derivative [19]. The proposed model had no limitations on the number of layers and the fractional-order was extended to an arbitrary real number bigger than 0.…”

Section: Methodsmentioning

confidence: 99%

Support Vector Machine optimization with fractional gradient descent for data classification

Hapsari

Utoyo

Purnami³

2021

JASMET

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Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions.

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Section: Methodsmentioning

confidence: 99%

Support Vector Machine optimization with fractional gradient descent for data classification

Hapsari

Utoyo

Purnami³

2021

JASMET

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“…These layers are successfully evaluating the oral features using a particular function that identifies the link between oral features and brain disease features. During this process, the system utilizes the back propagation network [39] used for feature training process that used is to minimize the deviation from the expected and computed oral cavity features. The representation of training is depicted in figure 6.…”

Section: Oral Cavity Linked Neurological Disorders Identification Usimentioning

confidence: 99%

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

et al. 2020

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Anatomically, oral cavity and central nervous system have a close relationship; the mouth and face are the location for 30-40% of the body's sensory and motor nerves. The identification of orofacial manifestations of neurological disorders is usually in direct relation with the responsibilities of a dental surgeon. Therefore, familiarizing dental surgeons with theses manifestations is essential to have better recognition, diagnosis, and correct decisions upon treating their associated Neurological Disorders. These manifestations should be efficiently analyzed using novel effective techniques since their related neurological disorders need to be early identified to avoid serious consequences. Furthermore, preventive dental care for patients with neurological disorders and all kind of rehabilitative treatments necessitates well-planned and effective novel approaches. The Internet of Medical Thing (IoMT) is a relatively new technology that has revolutionized health care sector drastically since last decade. It's a combination of medical sensors and wearable devices that can connect to health care storage systems via networking technologies allowing the transfer of medical data over a secure network. The data transferred are of utmost importance in diseases diagnosis and treatment. In this paper, an IoMT-based Intelligent Guided Particle Local Search with Optimized Neural Networks (IGPLONN) approach is proposed. Firstly, dental data are collected from the International Collaboration on Cancer Reporting (ICCR) oral cavity and central nervous system. Secondly, features are extracted from data and IGPLONN algorithm is utilized to select the effective features by minimizing the feature dimension that helps improve the overall prediction rate. Finally, the obtained features are transferred to the central health application through the IoMT platform where they can be analyzed by dental practitioners for neurological disorders prediction. The hybrid optimized technique improves the overall oral-linked neurological diseases detection rate. Moreover, it efficiently manages the forecast parameters that are used to predict the dental metastasis with minimum computational complexity. The performance of the proposed system has been experimentally evaluated on MATLAB to verify its excellence. The results revealed that proposed IoMT-based IGPLONN method attains the maximum accuracy of 98.3% compared to other methods. INDEX TERMS Oral cavity, neurological disorder, central nervous system, intelligent guided particle local search algorithm, neural network, Internet of Medical Things (IoMT).

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“…Recently, many systems are modeled as fractional-order systems utilizing fractional-order calculus. The fractional-calculus is found in several applications [34] in areas including electromagnetic waves [38], chemical engineering [39], signal processing [40], polymer science [41], electro-chemistry [42], neural networks [43], fluid mechanics [44], bio-engineering and bio sciences [45], control of power electronics [46], nonlinear control [47], etc.…”

Section: Fractional Order Calculusmentioning

confidence: 99%

Sine Cosine Algorithm Assisted FOPID Controller Design for Interval Systems Using Reduced-Order Modeling Ensuring Stability

Bokam

Patnana

Varshney³

et al. 2020

Algorithms

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The focus of present research endeavor was to design a robust fractional-order proportional-integral-derivative (FOPID) controller with specified phase margin (PM) and gain cross over frequency (ωgc) through the reduced-order model for continuous interval systems. Currently, this investigation is two-fold: In the first part, a modified Routh approximation technique along with the matching Markov parameters (MPs) and time moments (TMs) are utilized to derive a stable reduced-order continuous interval plant (ROCIP) for a stable high-order continuous interval plant (HOCIP). Whereas in the second part, the FOPID controller is designed for ROCIP by considering PM and ωgc as the performance criteria. The FOPID controller parameters are tuned based on the frequency domain specifications using an advanced sine-cosine algorithm (SCA). SCA algorithm is used due to being simple in implementation and effective in performance. The proposed SCA-based FOPID controller is found to be robust and efficient. Thus, the designed FOPID controller is applied to HOCIP. The proposed controller design technique is elaborated by considering a single-input-single-output (SISO) test case. Validity and efficacy of the proposed technique is established based on the simulation results obtained. In addition, the designed FOPID controller retains the desired PM and ωgc when implemented on HOCIP. Further, the results proved the eminence of the proposed technique by showing that the designed controller is working effectively for ROCIP and HOCIP.

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Fractional-Order Deep Backpropagation Neural Network

Cited by 49 publications

References 37 publications

Support Vector Machine optimization with fractional gradient descent for data classification

Support Vector Machine optimization with fractional gradient descent for data classification

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

Sine Cosine Algorithm Assisted FOPID Controller Design for Interval Systems Using Reduced-Order Modeling Ensuring Stability

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