• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.11
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• pp.430-435
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• 1985

Using the constructive algorithm proposed by Brayton and Tong, we analyze the stability of a modified coupled-form digital filter with quantization and overflow nonlinearities, and find the regions in the parameter plane where the filter is globally asymptotically stable. In these regions, the absence of zero-input limit cycles is ensured. This constructive algorithm gives less conservative stability results than the application of Jury-Lee stability criterion does.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.648-650
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• 1998

This paper proposes a constructive algorithm for generating the Takagi-Sugeno type fuzzy model through the sequential learning from training data set. The proposed algorithm has a two-stage learning scheme that performs both structure and parameter learning simultaneously. The structure learning constructs fuzzy model using two growth criteria to assign new fuzzy rules for given observation data. The parameter learning adjusts the parameters of existing fuzzy rules using the LMS rule. To evaluate the performance of the proposed fuzzy modeling approach, well-known benchmark is used in simulation and compares it with other modeling approaches.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.101-103
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• 2004

Expansible & Reconfigurable Neuro Informatics Engine(ERNIE) is effective in reconfigurability and extensibility. But ERNIE have the problem which have limited performance in initial network. To solve this problem, the constructive module using the reconfigurable ERNIE is implemented in simulation model. In this paper, simulation results on sonar data are showed that ERNIE using the constructive module obtains the better performance compared to ERNIE without it.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.77-85
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• 2015

This paper proposes a location algorithm that locates newly built p-facilities in the optimal area with minimum cost in a city of n districts. This problem has been classified as NP-hard, to which no polynomial time algorithm exists. The proposed algorithm improves the shortcomings of existing Myopic algorithm by constructing until p-facilities and exchanging locations of p-th facility for p=[1, n-1]. When applied to experimental data of n=5, 7, 10, 55, the proposed algorithm has obtained an approximate value nearest possible to the optimal solution take precedence of reverse-delete method. This algorithm is also simply executable using Excel.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.12-22
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• 2024

The travelling salesman problem is very famous and very difficult combinatorial optimization problem that has several applications in operations research, computer science and industrial engineering. As the problem is difficult, finding its optimal solution is computationally very difficult. Thus, several researchers have developed heuristic/metaheuristic algorithms for finding heuristic solutions to the problem instances. In this present study, a new hybrid genetic algorithm (HGA) is suggested to find heuristic solution to the problem. In our HGA we used comprehensive sequential constructive crossover, adaptive mutation, 2-opt search and a new local search algorithm along with a replacement method, then executed our HGA on some standard TSPLIB problem instances, and finally, we compared our HGA with simple genetic algorithm and an existing state-of-the-art method. The experimental studies show the effectiveness of our proposed HGA for the problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.574-579
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• 2009

There have been much difficulties to construct an optimized neural network in complex nonlinear regression problems such as selecting the networks structure and avoiding overtraining problem generated by noise. In this paper, we propose a stepwise constructive method for neural networks using a flexible incremental algorithm. When the hidden nodes are added, the flexible incremental algorithm adaptively controls the number of hidden nodes by a validation dataset for minimizing the prediction residual error. Here, the ELM (Extreme Learning Machine) was used for fast training. The proposed neural network can be an universal approximator without user intervene in the training process, but also it has faster training and smaller number of hidden nodes. From the experimental results with various benchmark datasets, the proposed method shows better performance for real-world regression problems than previous methods.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.193-201
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• 2023

In this paper, we consider the maximum scatter traveling salesman problem (MSTSP), a travelling salesman problem (TSP) variant. The problem aims to maximize the minimum length edge in a salesman's tour that travels each city only once in a network. It is a very complicated NP-hard problem, and hence, exact solutions can be found for small sized problems only. For large-sized problems, heuristic algorithms must be applied, and genetic algorithms (GAs) are found to be very successfully to deal with such problems. So, this paper develops a hybrid GA (HGA) for solving the problem. Our proposed HGA uses sequential sampling algorithm along with 2-opt search for initial population generation, sequential constructive crossover, adaptive mutation, randomly selected one of three local search approaches, and the partially mapped crossover along with swap mutation for perturbation procedure to find better quality solution to the MSTSP. Finally, the suggested HGA is compared with a state-of-art algorithm by solving some TSPLIB symmetric instances of many sizes. Our computational experience reveals that the suggested HGA is better. Further, we provide solutions to some asymmetric TSPLIB instances of many sizes.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.363-369
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• 2008

In this paper, we have proposed 3D shape reconstruction using superquadrics and single z-buffer Constructive Solid Geometry (CSG) rendering algorithm. Superquadrics can obtain various 3D model using 11 parameters and both superquadrics and deformed-superquadrics play a role of primitives which are consisted of CSG tree. In addition, we defined some effective equations using z-buffer algorithm and stencil buffer for synthesizing 3D model. Using this proposed algorithm, we need not to consider the coordinate of each 3D model because we simply compare the depth value of 3D model.

• Bulletin of the Korean Mathematical Society
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• v.38 no.2
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• pp.293-302
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• 2001

We improve the greedy algorithm which is one of the general convergence criterion for certain iterative sequence in a given space by building a constructive greedy algorithm on a normed linear space using an arithmetic average of elements. We also show the degree of approximation order is still $Ο(1\sqrt{\n}$) by a bounded linear functional defined on a bounded subset of a normed linear space which offers a good approximation method for neural networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.87-91
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• 2005

The cascade-correlation (CC) learning algorithm of Fahlman and Lebiere is one of the most influential constructive algorithm in a neural network. Cascading the hidden neurons results in a network that can represent very strong nonlinearities. Although this power is in principle useful, it can be a disadvantage if such strong nonlinearity is not required to solve the problem. 3 models are presented and compared empirically. All of them are based on valiants of the cascade architecture and output neurons weights training of the CC algorithm. Empirical results indicate the followings： (1) In the pattern classification, the model that train only new hidden neuron to output layer connection weights shows the best predictive ability; (2) In the function approximation, the model that removed input-output connection and used sigmoid-linear activation function is better predictability than CasCor algorithm.