Heuristic Self-organization Method Research Articles

Deep multi-layered GMDH-type neural network using revised heuristic self-organization and its application to medical image diagnosis of liver cancer

In this study, the deep multi-layered group method of data handling (GMDH)-type neural network algorithm using revised heuristic self-organization method is proposed and applied to medical image diagnosis of liver cancer. The deep GMDH-type neural network can automatically organize the deep neural network architecture which has many hidden layers. The structural parameters such as the number of hidden layers, the number of neurons in hidden layers and useful input variables are automatically selected to minimize prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The architecture of the deep neural network is automatically organized using the revised heuristic self-organization method which is a type of the evolutionary computation. This new neural network algorithm is applied to the medical image diagnosis of the liver cancer and the recognition results are compared with the conventional 3-layered sigmoid function neural network.

Medical Image Diagnosis of Lung Cancer by Deep Feedback GMDH-Type Neural Network

The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image diagnosis of lung cancer. The deep feedback GMDH-type neural network can identified very complex nonlinear systems using heuristic self-organization method which is a type of evolutionary computation. The deep neural network architectures are organized so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network. It is shown that the deep feedback GMDH-type neural network algorithm is useful for the medical image diagnosis of lung cancer because deep neural network architectures are automatically organized using only input and output data.

Medical Image Analysis of Brain X-ray CT Images By Deep GMDH-Type Neural Network

The deep Group Method of Data Handling (GMDH)-type neural network is applied to the medical image analysis of brain X-ray CT image. In this algorithm, the deep neural network architectures which have many hidden layers and fit the complexity of the nonlinear systems, are automatically organized using the heuristic self-organization method so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The learning algorithm is the principal component-regression analysis and the accurate and stable predicted values are obtained. The recognition results show that the deep GMDH-type neural network algorithm is useful for the medical image analysis of brain X-ray CT images.

Hybrid Multi-layered GMDH-type Neural Network Using Principal Component Regression Analysis and its Application to Medical Image Diagnosis of Liver Cancer

In this study, a hybrid multi-layered Group Method of Data Handling (GMDH)-type neural network algorithm using principal component-regression analysis is proposed and applied to the computer aided image diagnosis (CAD) of liver cancer. In the GMDH-type neural network, a heuristic self-organization method that is a type of evolutionary computation, is used to organize the neural network architecture. In this revised GMDH-type neural network, the optimum neural network architecture is automatically organized from three types of neural network architectures, such as the sigmoid function neural network, the radial basis function (RBF) network and the polynomial neural network architecture, by the heuristic self-organization method. Furthermore, the structural parameters such as the number of layers, the number of neurons in hidden layers and useful input variables, are automatically determined using the heuristic self-organization method. In the revised GMDH-type neural network proposed in this paper, the principal component-regression analysis is used to protect multi-colinearity which has occurred in the learning calculations of neurons, and accurate and stable prediction values are obtained. This new algorithm is applied to the medical image diagnosis of liver cancer. In this application, two types of neural network architectures fitting the complexity of the multi-detector row CT (MDCT) medical images, are automatically organized using the revised GMDH-type neural network algorithm The first neural network recognizes and extracts the liver regions from the MDCT images of the liver, and the second neural network recognizes and extracts the liver cancer regions. These results are compared with the conventional sigmoid function neural network trained using the back propagation method, and this GMDH-type neural network algorithm is shown to be useful for CAD of liver cancer.

Three-dimensional medical image analysis of the heart by the revised GMDH-type neural network self-selecting optimum neural network architecture

In this study, a revised group method of data handling (GMDH)-type neural network algorithm which self-selects the optimum neural network architecture is applied to 3-dimensional medical image analysis of the heart. The GMDH-type neural network can automatically organize the neural network architecture by using the heuristic self-organization method, which is the basic theory of the GMDH algorism. The heuristic self-organization method is a kind of evolutionary computation method. In this revised GMDH-type neural network algorithm, the optimum neural network architecture was automatically organized using the polynomial and sigmoid function neurons. Furthermore, the structural parameters, such as the number of layers, the number of neurons in the hidden layers, and the useful input variables, are selected automatically in order to minimize the prediction error criterion, defined as the prediction sum of squares (PSS).

Revised GMDH-type Neural Networks with Radial Basis Functions and their Application to Medical Image Recognition of Stomach

In this paper, the revised Group Method of Data Handling (GMDH)-type neural networks with radial basis functions are proposed. This algorithm has been developed based on the basic GMDH-type neural networks which have both characteristics of the GMDH, the conventional multi-layered neural networks and the multi-layered radial basis function networks. The GMDH-type neural networks are automatically organized by using the heuristic self-organization method proposed by A.G. Ivakhnenko. In the GMDH-type neural networks, many types of neurons such as the sigmoid function type, the radial basis function type, the high order Polynomial type and the linear function type, are used and these neurons are automatically selected so as to fit the complexity of the nonlinear system by using an error criterion defined as Akaike's Information Criterion (AIC). The revised GMDH-type neural networks with radial basis functions proposed in this paper can identify a complex nonlinear system more accurately compared with the conventional radial basis function networks. Revised GMDH-type neural networks with radial basis functions are applied to the medical image recognition of the stomach and it is shown that this algorithm is very useful method for the medical image recognition.

Global Optimization Based on Heuristic Self-Organization: The Discrete Case

Application of the heuristic self-organization method (or the so-called Group Method of Data Handling) to the solution of discrete optimization problems is considered. Convergence by probability of the self-organization process to a global optimization point is proved. Some examples of numerical experiments are presented.

A self-organization algorithm for real-time flood forecast

The group method of data handling (GMDH) algorithm presented by A. C. Ivakhnenko and colleagues is an heuristic self-organization method. It establishes the input–output relationship of a complex system using a multilayered perception-type structure that is similar to a feed-forward multilayer neural network. This study provides a step towards understanding and evaluating a role for GMDH in the investigation of the complex rainfall–runoff processes in a heterogeneous watershed in Taiwan. Two versions of the revised GMDH model are implemented: a stepwise regression procedure and a recursive formula. Eleven typhoon events in the Shen-cei Creek watershed, Taiwan, are used to build the model and verify its usefulness. The prediction results of the revised GMDH models and the instantaneous unit hydrograph (IUH) model are compared. Based on the criteria of forecasting precision and the rate and time of peak error, a much better performance is obtained with the revised GMDH models. Copyright © 1999 John Wiley & Sons, Ltd.

GMDH Type Neural Network Algorithm Identifying a Network Structure with the Heuristic Self-Organization Method

A GMDH type neural network algorithm which can identify a nonlinear system whose structure is very complex is proposed. In this algorithm, the heuristic self-organization method of the GMDH algorithm is used and so a neural network structure which has a good prediction accuracy can be organized automatically. Furthermore, instead of the partial polynomials which are used in the GMDH algorithm, partial-neural networks which have four layered hierarchical network structures are used to construct the GMDH type neural network. The proposed algorithm is applied to a nonlinear system identification problem and its advantage over the GMDH algorithm and the conventional networks is shown.

Use of heuristic self-organization method for mathematical description of flowmeter orifice plate intake edge wear

Use of heuristic self-organization method for mathematical description of flowmeter orifice plate intake edge wear

パルサーフィーダー型パルスカラムにおける軸方向ホールドアップ ＩＩ ＧＭＤＨによるモデル化と分析

In mathematical models for a pulsed perforated-plate column, the dispersed phase holdup has been considered to be uniform throughout the length of the column, but fairly recently it is treated as being nonuniform.In the previous paper, the axial holdup data were obtained in the dispersed aqueous and the dispersed organic modes. Experimental results showed that the axial holdup data become nonuniform throughout the column. It was also found that both of the plate type and the operation mode affected the axial holdup distribution.The present work is an attempt to formulate the axial holdup by means of a heuristic selforganization method that provides a nonlinear prediction model of complex system, since the holdup data did not directly show so significant trend as to formulate the axial holdup. The Group Method of Data Handling (GMDH) is used for this purpose. The GMDH can be used fbr selection and synthesis of input variables concerned with the axial holdup for the pulsed perforated-plate column. The axial holdup data have been successfully correlated and the identification models could be useful in discussing mathematical models.

Sequential GMDH Algorithm and Its Application to River Flow Prediction

A heuristic self-organization method for constructing a nonlinear river flow prediction model from the available data such as river flow and areal mean precipitation is presented. Our algorithm, the improved version of the proposed by A. G. Ivakhnenko, is useful for the prediction of complex nonlinear systems with a large number of variables and with a small amount of available input-output data. The efficiency and usefulness of the proposed prediction algorithm are shown by the use of a simulation model. This algorithm is applied to the flow prediction of the Karasu River in Japan. Numerical comparisons are performed between the prediction model by sequential GMDH and by the elaborate hydrologic methods, and we show that there are improvements in the newly introduced prediction algorithm for real-time computation.

逐次GMDHアルゴリズムとその河川流量予測への応用

A heuristic self-organization method for constructing a nonlinear river flow prediction model from the available data such as river flow and areal mean precipitation is presented. Our algorithm, the improved version of the proposed by A. G. Ivakhnenko, is useful for the prediction of complex nonlinear systems with a large number of variables and with a small amount of available input-output data. The efficiency and usefulness of the proposed prediction algorithm are shown by the use of a simulation model. This algorithm is applied to the flow prediction of the Karasu River in Japan. Numerical comparisons are performed between the prediction model by sequential GMDH and by the elaborate hydrologic methods, and we show that there are improvements in the newly introduced prediction algorithm for real-time computation.