• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1522-1522
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• 1999

This paper proposes the methods of short-term load forecasting using Kohonen neural networks and back-propagation neural networks. Historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Normal days and holidays are forecasted. For load forecasting in summer, max-, and min-temperature data are included in neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation. (1993-1997)

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.132-137
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• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.327-332
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• 2005

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Journal of Communications and Networks
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• 제18권4호
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• pp.619-628
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• 2016

This paper describes a theoretical framework for rate allocation to maximize the power efficiency of overall heterogeneous wireless networks whose users are assumed to have multihoming capabilities. Therefore, the paper first presents a power consumption model considering the circuit power and radio transmission power of each wireless network. Using this model, two novel power efficient rate allocation schemes (PERAS) for multihoming services are proposed. In this paper, the convex optimization problem for maximizing the power efficiency over wireless networks is formulated and solved while guaranteeing the required quality of service (QoS). Here, both constant bit rate and variable bit rate services are considered. Furthermore, we extend our theoretical framework by considering the sleep-operation management of wireless networks. The performance results obtained from numerical analysis reveal that the two proposed schemes offer superior performance over the existing rate allocation schemes for multihoming services and guarantee the required QoS.

• Journal of Communications and Networks
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• 제14권2호
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• pp.169-178
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• 2012

In this paper, we study the advantages of using range extension cooperative transmission (CT) in multi-hop energy harvesting wireless sensor networks (EH-WSNs) from the network layer perspective. EH-WSNs rely on harvested energy, and therefore, if a required service is energy-intensive, the network may not be able to support the service successfully. We show that CT networks that utilize both range extension CT and non-CT routing can successfully support services that cannot be supported by non-CT networks. For a two-hop toy network, we show that range extension CT can provide better services than non-CT. Then, we provide a method of determining the supportable services that can be achieved by using optimal non-CT and CT routing protocols for EH-WSNs. Using our method and network simulations, we justify our claim that CT networks can provide better services than nonCT networks in EH-WSNs.

• Transactions on Electrical and Electronic Materials
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• 제18권3호
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• pp.169-175
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• 2017

A safe and reliable protection system in distribution networks, specifically, those hosting distribution generation units, needs a robust over-current protection scheme. To avoid unintentional DG disconnection during fault conditions, a protection system should operate quickly and selectively. Therefore, to achieve this aim, satisfying coordination constraints are important for any protection scheme in distribution networks; these pose a challenging task in interconnected and large-scale networks. In this paper, a new coordination strategy, based on the same non-standard time-current curve for all relays, in order to find optimal coordination of directional over-current relays, is proposed. The main aim is to reduce violations, especially miscoordination between pair relays. Besides this, the overall time of operation of relays during primary and backup operations should be minimized concurrently. This work is being tackled based on genetic algorithms and motivated by the heuristic algorithm. For the numerical analysis, to show the superiority of this coordination strategy, the IEEE 30-bus test system, with a mesh structure and supplemented with distributed generation, is put under extensive simulations, and the obtained results are discussed in depth.

• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.12-17
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• 2002

We propose a new category of neurofuzzy networks- Self-organizing Neural Networks(SONN) with fuzzy polynomial neurons(FPNs) and discuss a comprehensive design methodology supporting their development. Two kinds of SONN architectures, namely a basic SONN and a modified SONN architecture are dicussed. Each of them comes with two types such as the generic and the advanced type. SONN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulation involves a series of synthetic as well as experimental data used across various neurofuzzy systems. A comparative analysis is included as well.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.214-217
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• 1999

In this paper, genetic algorithm (GA) is implemented to search for the optimal structures (i.e. the kind of neural networks, the number of inputs and hidden neurons) of neural networks which are used approximating a given nonlinear function. Two kinds of neural networks, i.e. the multilayer feedforward [1] and time delay neural networks (TDNN) [2] are involved in this paper. The synapse weights of each neural network in each generation are obtained by associated training algorithms. The simulation results of nonlinear function approximation are given out and some improvements in the future are outlined.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.401-403
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• 1992

For the control of robotic manipulators with unknown or uncertain dynamics, leaming control schemes are very effective control schemes for repeated trajectory following tasks. In this class of controllers, control techniques using neural networks have been gaining much attention in recent years.. In this note, we discuss the leaming control techniques using neural networks and propose a new model-based control scheme using multilayered neural networks. Three-layerd neural network is used as a feedback controller to compensate the mismatched terms between model plant and real plant. Computer simulations are performed to show the applicability and the limitation of the proposed controller.