• The Journal of Information Technology
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• v.8 no.1
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• pp.51-56
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• 2005

The Back propagation algorithm is a very effective supervised training method for multi-layer feed forward neural networks. This paper studies the decision boundary formation based on the Back propagation algorithm. The discriminating powers of several neural network topology are also investigated against five manually created data sets. It is found that neural networks with multiple hidden layer perform better than single hidden layer.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.462-462
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• 2000

In this paper, genetic algorithm (GA) is the technique to search for the optimal structures (i,e., the kind of neural network, the number of hidden neuron, ..) of the neural networks which are used approximating a given nonlinear function, In this paper, we used multi layer feed-forward neural network. The decision method of synapse weights of each neuron in each generation used back-propagation method. In this study, we simulated nonlinear function approximation in the temperature control system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.49-52
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• 1998

Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

• Journal of KIISE:Software and Applications
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• v.27 no.11
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• pp.1082-1087
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• 2000

신경회로망 구조의 정제(精製)는 회로망의 일반화능력이나 효율성의 관점에서 중요한 문제이다. 본 논문에서는 feed-forward neural networks로부터 은닉지식을 추출하는 방법을 사용하여 네트워크 재구성을 통한 정제방법을 제안한다. 먼저, 효율적인 if-then rule 추출방법을 제시하고 그 추출된 룰들을 사용하여 룰기반 네트워크로 변환하는 과정을 보여준다. 생성된 룰기반 네트워크 fully connected network에 비하여 상당히 축소된 연결 복잡도를 가지게 되며 일반적으로 더 우수한 일반화능력을 가지게 된다. 본 연구는 도메인 지식이 없이 데이타만 사용하여 어떻게 정제된 룰기반 신경망회로를 생성하고 있는가를 보여준다. 도메인 데이타들에 대한 실험결과도 제시하였다.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

• International Journal of Internet, Broadcasting and Communication
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• v.6 no.1
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• pp.9-12
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• 2014

We present a mathematical method for calculation of transmission zero locations, determining a filtering characteristics of two-port systems. By adjusting element values based on the zero locations, the frequency-selectivity is characterized. The characteristic polynomial of ladder networks in externally-loaded feed-forward systems is considered by adopting chain matrices for subsystems. This method can be extended to other types of lumped systems with cross-coupled sections. We find out the zeros by solving characteristics polynomials of closed-form expressions in terms of Laplace impedances of elements. The pairs of complex zeros are shown to be solely from the cross-coupled portion of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.73-77
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• 2002

The requirements for higher productivity call for high speed of the machine tool axes. Iron core type linear DC motor is growly accepted far a viable candidate of the high speed machine tool feed unit. LDM, however, has inherent disturbance force components: cogging and force ripple. These disturbance force directly affects tracking accuracy of the carrage and must be eliminated or reduced. Reducing motor ripple, this paper adapted the feed forward compensation method and neural network control. Experiments carried 7ut on the linear motor test setup show that this control methods is usable in order to reduce the motor ripple.

• Annual Conference of KIPS
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• 2006.05a
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• pp.1223-1226
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• 2006

P2P(Peer to Peer) 기술은 1990년대 후반기부터 산업계 및 학계에 주목을 받고 있는 기술 분야중의 하나로 이 기술의 장점은 인터넷 환경에 산재하여 있는 컴퓨팅 파워, 공간, 네트워크 대역을 인터넷 기반으로 효과적으로 활용하여 협력작업을 가능하게 한다는데 있다. 최근에는 모바일 환경 응용을 위한 P2P 디바이스 탐색 분야에 관심사가 증대되고 있으며, P2P 시스템은 중앙통제 장치가 결여 되어 있기 때문에 중앙통제 장치 개입을 최소로 하면서 P2P를 운영하기 위한 효율적인 기법 및 체계가 요구되고 있다. 본 논문에서는 기존의 접근방법을 검토하여 FFNN(feed forward neural network)을 이용한 디바이스 탐색 기법을 제시한다. 제시한 FFNN은 BP(back propagation) 알고리즘을 통해 훈련하고 디바이스를 탐색한다. 제시한 시스템의 성능을 보이기 위해 일정한 계산량을 가지는 작업을 에이전트를 활용, 탐색된 디바이스간에 분배하여 처리한다. 본 논문에서는 제한된 자원을 가지는 디바이스 간에 P2P를 사용하는 기법에 대해 제시하였다.

• Environmental Engineering Research
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• v.25 no.4
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• pp.515-521
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• 2020

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1($\frac{{\mu}g}{l}$), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.