“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

On robust estimation of the location parameter

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Time-Frequency Analysis.

The performance of recently developed neural network structure, general regression neural network (GRNN), is examined on the medical data. Pima Indian Dabetes (PID) data set is chosen to study on that had been examined by more complex neural network structures in the past. The results of early studies and of the GRNN structure presented in this paper is compared. Close classification accuracy to the reference work using ARTMAP-IC structured model, which is the best result obtained since now, is achieved by using GRNN, which has a simpler structure. The performance of the standard multilayer perceptron (MLP) and radial basis function (RBF) feed forward neural networks are also examined for the comparison as they are the most general and commonly used neural network structures. The performance of the MLP was tested for different types of backpropagation training algorithms.

Medical diagnosis on pima indian diabetes using general regression neural networks

Together with the increase in electronic circuit complexity, the design and optimization processes have to be automated with high accuracy. Predicting and improving the design quality in terms of performance, robustness and cost is the central concern of electronic design automation. Generally, optimization is a very difficult and time consuming task including many conflicting criteria and a wide range of design parameters. Particle swarm optimization (PSO) was introduced as an efficient method for exploring the search space and handling constrained optimization problems. In this work, PSO has been utilized for accommodating required functionalities and performance specifications considering optimal sizing of analog integrated circuits with high optimization ability in short computational time. PSO based design results are verified with SPICE simulations and compared to previous studies.

Analog circuit sizing via swarm intelligence

Breast cancer is the second largest cause of cancer deaths among women. The performance of the statistical neural network structures, radial basis network (RBF), general regression neural network (GRNN) and probabilistic neural network (PNN) are examined on the Wisconsin breast cancer data (WBCD) in this paper. This is a well-used database in machine learning, neural network and signal processing. Statistical neural networks are used to increase the accuracy and objectivity of breast cancer diagnosis.

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Breast cancer diagnosis using statistical neural networks

Proper interpretation of the thyroid gland functional data is an important issue on the diagnosis of thyroid disease The primary role of the thyroid gland is to help regulation of the body's metabolism Thyroid hormone produced by the thyroid gland provides this Production of too little thyroid hormone (hypo-thyroidism) or production of too much thyroid hormone (hyper-thyroidism) definites the type of thyroid disease In this work, various neural network methods have been used to help diagnosis of thyroid disease

Diagnosis of thyroid disease using artificial neural network methods

In analog filter design, component values are selected due to manufactured constant values where performing an exhaustive search on all possible combinations of preferred values for obtaining an optimized design is not feasible. The application of evolutionary algorithms (EA) in analog active filter circuit design and optimization is a promising area which is based on concepts of natural selection and survival of the fittest. In this paper, the performances of genetic algorithm, artificial bee colony optimization, and particle swarm optimization, which are nature-inspired EA techniques, are evaluated for active filter design. Each algorithm is applied to two different filter structures and performances of them are also evaluated when filter design is realized with components selected from different manufactured series.

Tulay Yildirim

Papers

Medical diagnosis on pima indian diabetes using general regression neural networks

Analog circuit sizing via swarm intelligence

Breast cancer diagnosis using statistical neural networks

Diagnosis of thyroid disease using artificial neural network methods

Performance Evaluation of Evolutionary Algorithms for Optimal Filter Design