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A novel family of online censoring based complex-valued least mean kurtosis algorithms

SIGNAL PROCESSING 2024年 216卷

作者： Guvenc, Buket colak Menguc, Engin Cemal Kayseri Univ Dept Elect & Elect Engn TR-38280 Kayseri Turkiye

In this paper, we propose a novel family of online censoring (OC) based complex-valued least mean kurtosis (CLMK) algorithms by inspiring the advantages of the online censoring strategy and kurtosis-based cost function. We first develop important members of this family of algorithms such as OC based CLMK (OC-CLMK) and augmented CLMK (OC-ACLMK). These algorithms censor less informative complex-valued data streams in an online manner and keep only the most informative ones for performing their weight vector updates. Thus, they reduce the costs of data processing without markedly affecting performance accuracy. However, they do not take into account possible outliers that disturb their performances considerably. Therefore, we also develop robust members such as robust OC-CLMK (ROC-CLMK) and OC-ACLMK (ROC-CLMK) algorithms, which censor less informative complex-valued data streams as well as possible outliers. The convergence analyses of the proposed algorithms in the sense of Lyapunov are also presented to derive the bounds of their step sizes in a compact form. The simulation results on large-scale system identification and regression scenarios affirm the mentioned attractive features of the proposed algorithms. This study also shows that the noteworthy properties of the proposed algorithms will provide important contributions to the processing of complex-valued big data streams.

关键词： Online censoring Complex-valued data streams Adaptive filter Least mean kurtosis learning algorithm Big data

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Bivariate fully fuzzy interpolation problem using artificial neural networks approach

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2016年第4期30卷 2267-2275页

作者： Hosseini, E. Jafarian, A. Islamic Azad Univ Dept Math Urmia Branch Orumiyeh Iran

Artificial neural networks modeling is one of the most prominent techniques for solving more complicated mathematical problems that can not be solved in the traditional computing environments. The work described here intends to offer an efficient bivariate fuzzy interpolation methodology based on the artificial neural networks approach. It has several notable features including high processing speeds and the ability to learn the solution to a problem from a set of examples which categorizes them in line of intelligent systems. To do this, a multilayer feed-forward neural architecture is depicted for constructing a fully fuzzy interpolating polynomial of arbitrary degree. Then, a back-propagation supervised learning optimization algorithm will be applied for estimating the unknown fuzzy coefficients of the solution polynomial. Finally, the advantage of our technique is illustrated by using some practical examples to show the ability of the improved algorithm in solving rigorous problems.

关键词： Bivariate fully fuzzy polynomial artificial neural network approximate solution criterion function learning algorithm

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Reaching optimized parameter set: protein secondary structure prediction using neural network

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NEURAL COMPUTING & APPLICATIONS 2017年第8期28卷 1947-1974页

作者： Dongardive, Jyotshna Abraham, Siby Univ Mumbai Dept Comp Sci Bombay Maharashtra India Univ Mumbai Dept Math & Stat GN Khalsa Coll Bombay Maharashtra India

We propose an optimized parameter set for protein secondary structure prediction using three-layer feed forward back propagation neural network. The methodology uses four parameters viz. encoding scheme, window size, number of neurons in the hidden layer and type of learning algorithm. The input layer of the network consists of neurons changing from 3 to 19, corresponding to different window sizes. The hidden layer chooses a natural number from 1 to 20 as the number of neurons. The output layer consists of three neurons, each corresponding to known secondary structural classes viz. alpha-helix, beta-strands and coil/turns, respectively. It also uses eight different learning algorithms and nine encoding schemes. Exhaustive experiments were performed using non-homologous dataset. The experimental results were compared using performance measures like Q(3), sensitivity, specificity, Mathew correlation coefficient and accuracy. The paper also discusses the process of obtaining a stabilized cluster of 2530 records from a collection of 11,340 records. The graphs of these stabilized clusters of records with respect to accuracy are concave, convergence is monotonic increasing and rate of convergence is uniform. The paper gives BLOSUM62 as the encoding scheme, 19 as the window size, 19 as the number of neurons in the hidden layer and one-step secant as the learning algorithm with the highest accuracy of 78 %. These parameter values are proposed as the optimized parameter set for the three-layer feed forward back propagation neural network for the protein secondary structure prediction.

关键词： Multi-layer feed forward network learning algorithm Proteins Hidden neuron Encoding scheme Performance measures Secondary structure prediction

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Optimal PID Controller Autotuning Design for MIMO Nonlinear Systems Based on the Adaptive SLP algorithm

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2021年第1期19卷 392-403页

作者： Pongfai, Jirapun Angeli, Chrissanthi Shi, Peng Su, Xiaojie Assawinchaichote, Wudhichai King Mongkuts Univ Technol Fac Engn Dept Elect & Telecommun Engn Bangkok Thailand Univ West Attica Fac Engn Dept Elect & Elect Engn Athens Greece Univ Adelaide Adelaide SA Australia Victoria Univ Melbourne Vic Australia Chongqing Univ Coll Automat Chongqing Peoples R China

In this paper, an adaptive swarm learning process (SLP) algorithm for designing the optimal proportional integral and derivative (PID) parameter for a multiple-input multiple-output (MIMO) control system is proposed. The SLP algorithm is proposed to improve the performance and convergence of PID parameter autotuning by applying the swarm algorithm and the learning process. The adaptive SLP algorithm improves the stability, performance and robustness of the traditional SLP algorithm to apply it to a MIMO control system. It can update the online weights of the SLP algorithm caused by the errors in the settling time, rise time and overshoot of the system based on a stable learning rate. The gradient descent is applied to update the weights. The stable learning rate is verified based on the Lyapunov stability theorem. Additionally, simulations are performed to verify the superiority of the algorithm in terms of performance and robustness. Results that compare the adaptive SLP algorithm with the traditional SLP, a neural network (NN), the genetic algorithm (GA), the particle swarm and optimization (PSO) algorithm and the kidney-inspired algorithm (KIA) based on a two-wheel inverted pendulum system are presented. With respect to performance and robustness, the adaptive SLP algorithm provides a better response than the traditional SLP, NN, GA, PSO and KIA.

关键词： Autotuning inverted pendulum learning algorithm multiple-input multiple-output (MIMO) optimal control PID controller swarm algorithm

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Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2018年 223卷 566-575页

作者： Ghritlahre, Harish Kumar Prasad, Radha Krishna Natl Inst Technol Dept Mech Engn Jamshedpur 831014 Jharkhand India

In the present study three different types of neural models: multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis function (RBF) has been used to predict the exergetic efficiency of roughened solar air heater. The experiments were conducted at NIT Jamshedpur, India, using two different types of absorber plate: arc shape wire rib roughened with relative roughness height 0.0395, relative roughness pitch 10 and angle of attack 60 degrees, and smooth absorber plates for 7 days. Total 210 data sets were collected from the experiments. Mass flow rate, relative humidity, wind speed, ambient air temperature, inlet air temperature, mean air temperature, average plate temperature and solar intensity were selected as input parameters in input layer to estimate the exergetic efficiency. In the first part of study, MLP model has been used. In this model 10-20 neurons with LM learning algorithm were used in hidden layer for optimal model selection. It has been found that LM-18 is an optimal model. In second part, GRNN model was used. The GRNN model was simulated experimentally at different spread constants and found that keeping spread constant as 1.5, optimal results have been obtained. In the third part, RBF model was used. For optimal model, 1-5 spread constant at interval of 0.5 have been used. It has been found that by taking spread constant 3.5, best results are obtained. In the last part of the study, all neural models are compared on the basis of statistical error analysis. It has been found that RBF model is better than GRNN and MLP models due to lowest value of RMSE and MAE and highest value of R-2 and ME. After RBF model, GRNN model performs better results as compared to MLP model. It has been found that the values of RMSE, MAE and R-2 were 0.001652, 2.86E-04 and 0.99999 respectively for RBF model.

关键词： Solar air heater Exergetic efficiency Artificial neural network learning algorithm Multi-layer perceptron Generalized regression neural network Radial basis function

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Synchronized Interactions in Spiked Neuronal Networks

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COMPUTER JOURNAL 2008年第6期51卷 723-730页

作者： Gelenbe, Erol Timotheou, Stelios Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn Intelligent Syst & Networks Grp London SW7 2BT England

The study of artificial neural networks has originally been inspired by neurophysiology and cognitive science. It has resulted in a rich and diverse methodology and in numerous applications to machine intelligence, computer vision, pattern recognition and other applications. The random neural network (RNN) is a probabilistic model which was inspired by the spiking behaviour of neurons, and which has an elegant mathematical treatment that provides both its steady-state behaviour and offers efficient learning algorithms for recurrent networks. Second-order interactions, where more than one neuron jointly act upon other cells, have been observed in nature;they generalize the binary (excitatory-inhibitory) interaction between pairs of cells and give rise to synchronous firing (SF) by many cells. In this paper, we develop an extension of the RNN to the case of synchronous interactions, which are based on two cells that jointly excite a third cell;this local behaviour is in fact sufficient to create SF by large ensembles of cells. We describe the system state and derive its stationary solution as well as a O(N-3) gradient descent learning algorithm for a recurrent network with N cells when both standard excitatory-inhibitory interactions, as well as SF, are present.

关键词： random neural network synchronized interactions learning algorithm

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Self-organising networks in modelling experimental data in software engineering

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IEE PROCEEDINGS-COMPUTERS AND DIGITAL TECHNIQUES 2002年第3期149卷 61-78页

作者： Oh, SK Pedrycz, W Park, HS Wonkwang Univ Sch Elect & Elect Engn Iksan 570749 Chonbuk South Korea Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G6 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

Experimental Software datasets describing Software projects in terms of their complexity and development time have been the subject of intensive modelling. A number of various modelling methodologies and detailed modelling designs hake been proposed including neural networks and fuzzy models. The authors introduce self-organising networks (SON) that result from a synergy of fuzzy inference schemes and polynomial neural networks (PNNs). The latter has included an efficient scheme of selecting input variables of the model being realised on a basis of a group method of data handling (GMDH) algorithm. The authors discuss a detailed architecture of the SON and propose a comprehensive learning algorithm. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SON are not predetermined (as is the case in a popular topology of a multilayer perceptron). The experimental results include well-known software data such as the one describing software modules of the medical imaging system (MIS) and the NASA data set concerning software cost estimation. The experimental results reveal that the proposed model exhibits high accuracy.

关键词： learning in AI polynomial neural networks neural networks Neural nets Knowledge engineering techniques software engineering fuzzy inference schemes medical imaging system fuzzy logic experimental data modelling self-organising networks performance index group method of data handling algorithm learning (artificial intelligence) fuzzy models Formal logic self-organising feature maps learning algorithm Software engineering techniques experimental software datasets

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Computational neural networks for predictive microbiology .1. Methodology

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INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY 1997年第1期34卷 27-49页

作者： Najjar, YM Basheer, IA Hajmeer, MN KANSAS STATE UNIV DEPT ANIM SCI & INDMANHATTANKS 66506

Artificial neural networks are mathematical tools inspired by what is known about the physical structure and mechanism of the biological cognition and learning. Neural networks have attracted considerable attention due to their efficacy to model wide spectrum of challenging problems. In this paper, we present one of the most popular networks, the backpropagation, and discuss its learning algorithm and analyze several issues necessary for designing optimal networks that can generalize after being trained on examples. As an application in the area of predictive microbiology, modeling of microorganism growth by neural networks will be presented in a second paper of this series.

关键词： neural networks backpropagation learning algorithm microbial growth modeling

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Evaluation of fully fuzzy regression models by fuzzy neural network

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NEURAL COMPUTING & APPLICATIONS 2012年第s1期21卷 S105-S112页

作者： Mosleh, M. Allahviranloo, T. Otadi, M. Islamic Azad Univ Dept Math Sci & Res Brench Tehran 14778 Iran Islamic Azad Univ Dept Math Firoozkooh Branch Firoozkooh Iran

In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy linear and nonlinear regression models with fuzzy output and fuzzy inputs, is presented. Here, a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.

关键词： Fuzzy neural networks Fuzzy number learning algorithm Fuzzy regression

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IMPROVING RECALL IN ASSOCIATIVE MEMORIES BY DYNAMIC THRESHOLD

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NEURAL NETWORKS 1994年第9期7卷 1379-1385页

作者： WANG, T Zhejiang University P.R. China

In this paper, a simple learning method and a dynamic threshold concept for associative memories (AMs) is presented. The learning approach is designed to store all training patterns with basins of attraction as large as possible. After the learning process stops, the dynamic threshold introduces a threshold in the recall phase. It can reduce the probability of converging to spurious states. A large number of computer simulations are implemented to show the improved recalls.

关键词： NEURAL NETWORK ASSOCIATIVE MEMORY learning algorithm BASIN OF ATTRACTION RECALL PHASE DYNAMIC THRESHOLD SPURIOUS STATE HAMMING DISTANCE

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