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Epilepsy diagnosis using artificial neural network learned by PSO

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2015年第2期23卷 421-432页

作者： Yalcin, Nesibe Tezel, Gulay Karakuzu, Cihan Bilecik Seyh Edebali Univ Dept Comp Engn Bilecik Turkey Sakarya Univ Inst Nat Sci Sakarya Turkey Selcuk Univ Dept Comp Engn Konya Turkey

Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.

关键词： Artificial neural networks backpropagation algorithm electroencephalogram epilepsy diagnosis particle swarm optimization

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A novel weight pruning method for MLP classifiers based on the MAXCORE principle

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NEURAL COMPUTING & APPLICATIONS 2013年第1期22卷 71-84页

作者： Medeiros, Claudio M. S. Barreto, Guilherme A. Fed Inst Ceara Dept Ind BR-60040531 Fortaleza Ceara Brazil Univ Fed Ceara Dept Teleinformat Engn Ctr Technol BR-60455970 Fortaleza Ceara Brazil

We introduce a novel weight pruning methodology for MLP classifiers that can be used for model and/or feature selection purposes. The main concept underlying the proposed method is the MAXCORE principle, which is based on the observation that relevant synaptic weights tend to generate higher correlations between error signals associated with the neurons of a given layer and the error signals propagated back to the previous layer. Nonrelevant (i.e. prunable) weights tend to generate smaller correlations. Using the MAXCORE as a guiding principle, we perform a cross-correlation analysis of the error signals at successive layers. Weights for which the cross-correlations are smaller than a user-defined error tolerance are gradually discarded. Computer simulations using synthetic and real-world data sets show that the proposed method performs consistently better than standard pruning techniques, with much lower computational costs.

关键词： MLP classifier backpropagation algorithm Weight pruning Feature selection

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Real-time surface discrimination using an analog neural network implemented in a phase-shift laser rangefinder

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IEEE SENSORS JOURNAL 2007年第9-10期7卷 1381-1387页

作者： Gatet, Laurent Tap-Beteille, Helene Lescure, Marc ENSEEIHT LOSE F-31071 Toulouse France

An analog neural network (NN) was developed for real-time surface recognition by using two photoelectrical signals issued from a phase-shift rangefinder. The NN architecture consists of a multilayer perceptron (MLP) with two inputs, three neurons in the hidden layer, and one output. The NN output is compared with threshold voltages in order to classify the tested surfaces. In this type of application, analog NN implementation has many advantages, especially the small silicon area used, a low-power consumption, and no analog-to-digital conversions. This recognition system has been successfully tested for four types of surfaces (a plastic surface, a glossy paper, a painted wall, and a porous surface), at a remote distance between the rangefinder and the target varying from 0.5 in up to 1.25 in and with a laser beam incidence angle varying between -pi/6 and pi/6. This paper presents the NN training and the experimental tests of surface discrimination.

关键词： analog neural network (NN) backpropagation algorithm laser rangetinder multilayer perceptron (MLP) surface detection

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Modelling, simulation, and optimization of diabetes type II prediction using deep extreme learning machine

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2020年第2期12卷 125-138页

作者： Rehman, Abdur Athar, Atifa Khan, Muhammad Adnan Abbas, Sagheer Fatima, Areej Atta-ur-Rahman Saeed, Anwaar Natl Coll Business Adm & Econ Dept Comp Sci Lahore Pakistan Comsats Univ Islamabad Dept Comp Sci Lahore Campus Lahore Pakistan Lahore Garrison Univ Dept Comp Sci Lahore Pakistan Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia Virtual Univ Dept Comp Sci Islamabad Pakistan

Diabetes is among the most common medical issues which people are facing nowadays. It may cause physical incapacity or even death in some cases. It has two core types, namely type I and type II. Both types are chronic and influence the functions of the human body that regulate blood sugar. In the human body, glucose is the main element that boosts cells. However, insulin is a key that enters the cells to control blood sugar. People with diabetes type I do not have the ability to produce insulin. Whereas people with diabetes type II lack the ability to react to insulin and frequently do not make enough insulin. For adequate analysis of such a fatal disease, techniques with a minimum error rate must be utilized. In this regard, different models of artificial neural network (ANN) have been investigated in the literature to diagnose/predict the condition with a minimum error rate, however, there is a need for improvement. To further advance the accuracy, a deep extreme learning machine (DELM) based prediction model is proposed and investigated in this research. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows significant improvement in results compared to previous investigations. It is observed that during the investigation the proposed approach has the highest accuracy rate of 92.8% with 70% of training (9500 samples) and 30% of test and validation (4500 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

关键词： DELM ANN feedforward backpropagation algorithm diabetes prediction

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An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch's problem

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NEURAL COMPUTING & APPLICATIONS 2017年第1期28卷 171-178页

作者： Yadav, Neha Yadav, Anupam Kumar, Manoj Kim, Joong Hoon Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea Natl Inst Technol Dept Sci & Humanities Garhwal 246174 Uttarakhand India Motilal Nehru Natl Inst Technol Dept Math Allahabad 211004 Uttar Pradesh India

In this article, a simple and efficient approach for the approximate solution of a nonlinear differential equation known as Troesch's problem is proposed. In this article, a mathematical model of the Troesch's problem is described which arises in confinement of plasma column by radiation pressure. An artificial neural network (ANN) technique with gradient descent and particle swarm optimization is used to obtain the numerical solution of the Troesch's problem. This method overcomes the difficulty arising in the solution of Troesch's problem in the literature for eigenvalues of higher magnitude. The results obtained by the ANN method have been compared with the analytical solutions as well as with some other existing numerical techniques. It is observed that our results are more approximate and solution is provided on continuous finite time interval unlike the other numerical techniques. The main advantage of the proposed approach is that once the network is trained, it allows evaluating the solution at any required number of points for higher magnitude of eigenvalues with less computing time and memory.

关键词： Artificial neural network technique backpropagation algorithm Plasma column Particle swarm optimization

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Twisted Quaternary Neural Networks

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第4期7卷 397-401页

作者： Kobayashi, Masaki Nakajima, Akihiro Univ Yamanashi Interdisciplinary Grad Sch Med & Engn Kofu Yamanashi 4008511 Japan

The quaternary neural network (QNN) proposed by Nitta is a high-dimensional neural network. Nitta showed that its learning is faster than that of ordinary neural networks and the number of required parameters is almost one-third of that of real-valued neural networks by computer simulations. In this paper, we propose the twisted quaternary neural network (TQNN) which modifies the directions of multiplications of the QNN. Since quaternions are noncommutative on multiplication, we can get another neural network. When the activation function is linear, multilayered neural networks can be expressed by single-layered neural networks. But the TQNN cannot be expressed by a single-layered QNN even if the activation function is linear. Therefore, the TQNN is expected to produce a variety of signal-processing systems. We performed computer simulations to compare the QNN and the TQNN. Then we found that the latter's learning is a little faster. Moreover, computer simulation showed that the QNN tended to be trapped in local minima or plateaus but the TQNN did not. It is said that reducibility causes local minima and plateaus. We discuss the differences of reducibility between the QNN and the TQNN as well. (c) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： neural networks quaternion learning backpropagation algorithm

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Compact building blocks for artificial neural networks

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ELECTRONICS LETTERS 1999年第1期35卷 56-57页

作者： Meléndez-Rodríguez, M Silva-Martínez, J Natl Inst Astrophys Opt & Elect Integrated Circuit Design Grp Puebla 72000 Mexico

A four quadrant analogue multiplier, an activation function and a differentiator intended for the implementation of backpropagation algorithms are proposed. The priorities for the design are low-power, low-voltage, an... 详细信息

关键词： differentiating circuits neural nets artificial neural network Neural net devices Neural nets (circuit implementations) analogue multipliers silicon area activation function analogue multiplier backpropagation differentiator backpropagation algorithm low-power electronics low-power low-voltage circuit building block

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Experimental Investigation on the Influence of Graphene Nanoplatelets Dispersion on the Thermal Conductivity of Sunflower Oil

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INTERNATIONAL JOURNAL OF NANOSCIENCE 2020年第2期19卷

作者： Muthuraj, M. Raj, J. Bensam Sunil, J. Vidyaa Vikas Coll Engn & Technol Dept Mech Engn Namakal 637214 Tamil Nadu India Nadar Saraswathi Coll Engn & Technol Dept Mech Engn Theni 625531 Tamil Nadu India VV Coll Engn Dept Mech Engn Tirunelveli 628656 Tamil Nadu India

In the past decades, considerable efforts have been made for the development of energy-efficient and eco-friendly convective heat transfer and lubricating agents because of growing energy demands, precision manufacturing, miniaturization and sustainability issues. In this study, different concentrations of graphene-sunflower oil nanofluid were prepared and their thermal conductivity was experimentally investigated and compared with the correlations of similar researches found in the literature. The morphology of graphene nanoplatelets was appraised by X-ray diffractometer (XRD) and scanning electron microscope (SEM). The results show that the thermal conductivity of nanofluid was enhanced with temperature and nanoparticles weight fraction. The nanoconvection at high temperatures, less meandering mobility of graphene nanoplatelets and high kinematic viscosity of graphene nanofluids at low temperatures were identified as the key factors for the thermal conductivity enhancement. Further, the concentration and temperature-dependent theoretical correlation were proposed for estimating the thermal conductivity of graphene nanofluids using backpropagation algorithm of artificial neural network (ANN) with the minimum margin of deviation.

关键词： Eco-friendly thermal conductivity graphene nanoplatelets kinematic viscosity backpropagation algorithm artificial neural network

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Stacking-based neural network for nonlinear time series analysis

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STATISTICAL METHODS AND APPLICATIONS 2024年第3期33卷 901-924页

作者： De Alwis, Tharindu P. Samadi, S. Yaser Worcester Polytech Inst Dept Math Sci Worcester MA USA Southern Illinois Univ Carbondale Sch Math & Stat Sci Carbondale IL 62901 USA

Stacked generalization is a commonly used technique for improving predictive accuracy by combining less expressive models using a high-level model. This paper introduces a stacked generalization scheme specifically designed for nonlinear time series models. Instead of selecting a single model using traditional model selection criteria, our approach stacks several nonlinear time series models from different classes and proposes a new generalization algorithm that minimizes prediction error. To achieve this, we utilize a feed-forward artificial neural network (FANN) model to generalize existing nonlinear time series models by stacking them. Network parameters are estimated using a backpropagation algorithm. We validate the proposed method using simulated examples and a real data application. The results demonstrate that our proposed stacked FANN model achieves a lower error and improves forecast accuracy compared to previous nonlinear time series models, resulting in a better fit to the original time series data.

关键词： Stacked generalization Cross-validation Time series Feed-forward artificial neural network (FANN) backpropagation algorithm C4 Econometric and Statistical Methods: Special Topics C45 Neural Networks and Related Topics

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Chaos and neural network learning. Some observations

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NEURAL PROCESSING LETTERS 1998年第2期7卷 69-80页

作者： Bertels, K Neuberg, L Vassiliadis, S Pechanek, DG Univ Namur Dept Business Adm B-5000 Namur Belgium Tech Univ Delft Dept Elect Engn NL-2628 CD Delft Netherlands IBM Corp Microelect Div Res Triangle Pk NC 27709 USA

In this paper, we investigate the impact of chaos on the learning process of the XOR-boolean function by backpropagation neural networks. It has been shown previously that such networks exhibit chaotic behavior but it has never been studied whether chaos enhances or prohibits learning. We show that chaos (when learning the XOR-boolean function) does indeed allow learning but our findings do not indicate any positive role of chaos for learning. In particular, we found that the temperature parameter in the backpropagation algorithm causes the parameter regime, as represented by means of a bifurcation diagram, to shift to the right. We furthermore found that as less chaos appears during the learning process, the faster, on the average, a neural network learned the XOR-function.

关键词： backpropagation algorithm bifurcation chaos neural network learning parametrization

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