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New semi-analytical solutions of the time-fractional Fokker-Planck equation by the neural network method

OPTIK 2022年 259卷

作者： Wei, Jia-Li Wu, Guo-Cheng Liu, Bao-Qing Zhao, Zhengang Nanjing Univ Finance & Econ Sch Appl Math Nanjing 210023 Jiangsu Peoples R China Neijiang Normal Univ Coll Math & Informat Sci Data Recovery Key Lab Sichuan Prov Neijiang 641100 Peoples R China Shanghai Customs Coll Dept Fundamental Courses Shanghai 201204 Peoples R China

This paper suggests a neural network method for solving the time-fractional Fokker-Planck equation. An energy function is constructed by means of initial-boundary value conditions. The Caputo derivative is approximated by L-1 numerical scheme and an unconstrained discretization minimization problem is presented. gradient descent algorithm is adopted for neural network training. Semi-analytical solutions are obtained where two numerical examples demonstrate the method's efficiency.

关键词： Fractional Fokker-Planck equation L-1 numerical scheme Neural network gradient descent algorithm

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Z-Adaptive Fuzzy Inference Systems

<i>Z</i>-Adaptive Fuzzy Inference Systems

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IEEE CIS International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Rezaee-Ahmadi, Fatemeh Rafiei-R, Hamed Akbarzadeh-T, Mohammad-R Ferdowsi Univ Mashhad Dept Elect Engn Ctr Excellence Soft Comp & Intelligent Informat P Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781665444071

Z-numbers consist of two components, restriction and restriction reliability, to cover both possibilistic and probabilistic uncertainties. So far, the components of Z-numbers are merely determined by expert knowledge and lack automated learning/training. To overcome this limitation, we propose a Z-Adaptive Fuzzy Inference System (ZAFIS) that systematically learns the parameters of Z-numbers from input-output data pairs. We first convert the second component of Z-numbers to a crisp number. We then use this number as a weight for the first fuzzy membership part of Z-numbers. Finally, the resultant membership is placed in a fuzzy inference system, and the parameters of the system are learned based on the input-output data pairs using a gradient descent algorithm. The proposed method is evaluated on several functions (sine, increasing sine, Hermite, Gabor, and a nonlinear function) with/without added noise scenarios. The results show that the ZAFIS is more robust against the noisy inputs and is superior to the Fuzzy Inference Systems (FISs) in terms of MSE.

关键词： Z-numbers Fuzzy inference systems Uncertainty Reliability gradient descent algorithm

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Cascaded Internal Phase Control of All-Fiber Coherent Fiber Laser Array

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FRONTIERS IN PHYSICS 2022年 10卷

作者： Chang, Hongxiang Su, Rongtao Zhang, Yuqiu Jiang, Min Chang, Qi Long, Jinhu Ma, Pengfei Ma, Yanxing Zhou, Pu Natl Univ Def Technol Coll Adv Interdisciplinary Studies Changsha Peoples R China

Fiber lasers have been widely used in medical care, industries, and scientific research in recent years. The coherent beam combining of fiber lasers with an internal phase control has drawn many interests at present, which is a promising method to achieve a large-scale optical phased array. In this article, we presented a cascaded internal phase control method to expand the internal all-fiber phased array. The method distributes the phase measurements to a series of internal Mach-Zender interferometers. Then, the phase of each loop is locked by the gradient descent algorithm. The electric control method to compensate pi-ambiguity between channels is proposed. Finally, the phases of the three fiber beams are locked experimentally to verify the feasibility of the method, and the residue phase error is better than lambda/22.

关键词： coherent beam combining internal phase-locking fiber laser array gradient descent algorithm laser field manipulation

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Self-Configuring and Reconfigurable Silicon Photonic Signal Processor

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ACS PHOTONICS 2020年第3期7卷 792-799页

作者： Zhou, Hailong Zhao, Yuhe Wang, Xu Gao, Dingshan Dong, Jianji Zhang, Xinliang Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Sch Opt & Elect Informat Wuhan 430074 Peoples R China Hong Kong Polytech Univ Photon Res Ctr Dept Elect & Informat Engn Hong Kong Peoples R China

Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, a self-configuring and fully reconfigurable silicon photonic signal processor is proposed and experimentally demonstrated. The proposed photonic signal processor is capable of performing various functions, including multichannel optical switching, optical multiple-input-multiple-output descrambler, and tunable optical filter. All the functions are achieved by complete self-configuration without knowing the inner structure. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with the self-configuring ability.

关键词： silicon photonics optical signal processing gradient descent algorithm programmable photonics

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Prediction of PM2.5 concentration based on weighted bagging and image contrast-sensitive features

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2020年第3-4期34卷 561-573页

作者： Qiao, Junfei He, Zengzeng Du, Shengli Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

In this paper, we propose an effective method of PM2.5 prediction based on image contrast-sensitive features and weighted bagging based neural network (WBBNN). Three types of image contrast-sensitive features are first extracted from the images and fuzzified. Next, a weighted bagging strategy combining the ensemble fuzzy neural network (FNN) and ensemble radial basis function neural network (RBFNN) is established. The ensemble neural network (NN), regardless of FNN and RBFNN, is obtained by simply averaging the outputs of component neural networks. And these component neural networks are trained by the improved gradient descent algorithm and samples acquired by bootstrap sampling. Finally, the WBBNN is used to forecast PM2.5 concentration by extracting three types of image contrast-sensitive features. Results of experiments demonstrate that our prediction method is more reliable, practical and efficient than FNN, RBFNN, the ensemble NNs, and state-of-the-art quality assessment method in terms of predicting the concentration of PM2.5 More importantly, an improved gradient descent algorithm is developed to accelerate the convergence speed and ensure the prediction accuracy of WBBNN and the fuzzy features acquired by the feature fuzzification method can greatly improve the robustness and precision of WBBNN.

关键词： PM2.5 prediction Weighted bagging strategy Bootstrap sampling Contrast-sensitive feature Ensemble neural network gradient descent algorithm

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Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2020年第4期14卷 759-770页

作者： Anderlucci, Laura Viroli, Cinzia Univ Bologna Dept Stat Sci Via Belle Arti 41 I-40126 Bologna Italy

Topic detection in short textual data is a challenging task due to its representation as high-dimensional and extremely sparse document-term matrix. In this paper we focus on the problem of classifying textual data on the base of their (unique) topic. For unsupervised classification, a popular approach called Mixture of Unigrams consists in considering a mixture of multinomial distributions over the word counts, each component corresponding to a different topic. The multinomial distribution can be easily extended by a Dirichlet prior to the compound mixtures of Dirichlet-Multinomial distributions, which is preferable for sparse data. We propose a gradient descent estimation method for fitting the model, and investigate supervised and unsupervised classification performance on real empirical problems.

关键词： Clustering gradient descent algorithm Mixture models Text data analysis

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High-quality depth up-sampling via a supervised classification guided MRF model

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PATTERN RECOGNITION LETTERS 2020年 138卷 315-320页

作者： Qiao, Yiguo Jiao, Licheng Tang, Xu Li, Wenbin Cosker, Darren Xidian Univ Int Res Ctr Intelligent Percept & Computat Sch Artificial IntelligenceMinist EducJoint Int Key Lab Intelligent Percept & Image Understanding 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Univ Bath Ctr Anal Mot Entertainment Res & Applicat CAMERA Bath BA2 7AY Avon England

In this paper, a Supervised Classification assisted Markov Random Field (SC-MRF) model is proposed for generating high-quality up-sampled depth maps. The proposed model aims to reduce depth bleeding and depth confusion artifacts that can be produced at boundary regions of the up-sampled depth maps. In the proposed model, segmentation of low-resolution (LR) depth map is first used to supervise the classification of corresponding high-resolution (HR) color image. With this supervised classification, not only can the depth edges be retained, but redundant textures in the HR color image can be omitted. The classification result is then introduced into the design of a MRF energy function, and the final up-sampled depth map is obtained by optimizing this energy function with the gradient descent algorithm. For simplicity, classical K-means clustering is adopted to segment the LR depth map into several classes, and a feature-based K-nearest neighbour (K-NN) method is utilized for the supervised classification. With the proposed SC-MRF model, interaction between depths of different classes will be strongly suppressed, meaning depth edges are well preserved. Comparisons with the state-of-the-art demonstrate the strong performance of the proposed method both visually and by quantitative evaluation. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Markov random fields K-means clustering K-NN Supervised classification gradient descent algorithm

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A Comparative Performance of Optimizers and Tuning of Neural Networks for Spoof Detection Framework

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第4期13卷 884-890页

作者： Chadha, Ankita Abdullah, Azween Angeline, Lorita Taylors Univ Sch Comp Sci Subang Jaya 47500 Selangor Malaysia

The breakthroughs in securing speaker verification systems have been challenging and yet are explored by many researchers over the past five years. The compromise in security of these systems is due to naturally sounding synthetic speech and handiness of the recording devices. For developing a spoof detection system, the back-end classifier plays an integral role in differentiating spoofed speech from genuine speech. This work conducts the experimental analysis and comparison of up-to-date optimization techniques for a modified form of Convolutional Neural Network (CNN) architecture which is Light CNN (LCNN). The network is standardized by exploring various optimizers such as Adaptive moment estimation, and other adaptive algorithms, Root Mean Square propagation and Stochastic gradient descent (SGD) algorithms for spoof detection task. Furthermore, the activation functions and learning rates are also tested to investigate the hyperparameter configuration for faster convergence and improving the training accuracy. The counter measure systems are trained and validated on ASV spoof 2019 dataset with Logical (LA) and Physical Access (PA) attack data. The experimental results show optimizers perform better for LA attack in contrast to PA attack. Additionally, the lowest Equal Error Rate (EER) of 9.07 is obtained for softmax activation with SGD with momentum wrt LA attack and 9.951 for SGD with nestrov wrt PA attack.

关键词： Spoof detection speech synthesis voice conversion convolutional neural networks optimizers gradient descent algorithm spoofed speech automatic speaker verification

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A new convolutive source separation approach for independent/dependent source components

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DIGITAL SIGNAL PROCESSING 2020年 100卷 102701-000页

作者： Mamouni, N. Keziou, A. Fenniri, H. Ghazdali, A. Hakim, A. Univ Reims LMR UMR 9008 CNRS Reims France Univ Reims CReSTIC Reims France Univ Sultan Moulay Slimane ENSA Khouribga LIPIM Beni Mellal Morocco Univ Cadi Ayyad FST LAMAI Marrakech Morocco

In this paper, a new source separation approach, for linear convolutive mixtures of independent/dependent source components, is presented. It consists in minimizing an appropriate separation criterion, measuring the difference between the nonparametric copula density of the estimated sources and semiparametric copula densities modeling the dependency structure of the source components. The proposed approach represents an efficient tool for separating linear convolutive mixtures, especially, when the source components are statistically dependent, if prior information about the dependency structure of the source components is available. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Convolutive source separation Dependent source components Kullback-Leibler divergence between copula densities Copula model selection Statistical estimation gradient descent algorithm

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Taylor-DBN: A new framework for speech recognition systems

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2021年第2期19卷 2050071-2050071页

作者： Haridas, Arul Valiyavalappil Marimuthu, Ramalatha Sivakumar, V. G. Chakraborty, Basabi Sathyabama Inst Sci & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Kumaraguru Coll Technol Dept Elect & Commun Engn Coimbatore 641049 Tamil Nadu India Vidya Jyothi Inst Technol Dept Elect & Commun Engn Hyderabad Telangana India Iwate Prefectural Univ Fac Software & Informat Sci Takizawa Japan

Speech recognition is a rapidly emerging research area as the speech signal contains linguistic information and speaker information that can be used in applications including surveillance, authentication, and forensic field. The performance of speech recognition systems degrades expeditiously nowadays due to channel degradations, mismatches, and noise. To provide better performance of speech recognition, the Taylor-Deep Belief Network (Taylor-DBN) classifier is proposed, which is the modification of the gradient descent (GD) algorithm with Taylor series in the existing DBN classifier. Initially, the noise present in the speech signal is removed through the speech signal enhancement. The features, such as Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS), spectral kurtosis, and spectral skewness, are extracted from the enhanced speech signal, which is fed to the Taylor-DBN classifier that identifies the speech of the impaired persons. The experimentation is done using the TensorFlow speech recognition database, the real database, and the ESC-50 dataset. The accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Mean Square Error (MSE) of the Taylor-DBN for TensorFlow speech recognition database are 96.95%, 3.04%, 3.04%, and 0.045, respectively, and for real database, the accuracy, FAR, FRR, and MSE are 96.67%, 3.32%, 3.32%, and 0.0499, respectively. Similarly, for the ESC-50 dataset, the accuracy, FAR, FRR, and MSE are 96.81%, 3.18%, 3.18%, and 0.047, respectively. The results imply that the Taylor-DBN provides better performance as compared to the existing conventional methods.

关键词： Speech recognition Taylor series deep belief network gradient descent algorithm spectral kurtosis spectral skewness

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