13. 벼잎선충의 약제처리 방법별 방제 효과

A survey of journal articles on neural network business applications published between 1988 and 1995 indicates that an increasing amount of neural network research is being conducted for a diverse range of business activities. The classification of literature by (1) year of publication, (2) application area, (3) problem domain, (4) decision process phase, (5) level of management, (6) level of task interdependence, (7) means of development, (8) corporate/academic interaction in development, (9) technology integration, (10) comparative study, (11) major contribution, and (12) journal provides some insights into the trends in neural networks research. The implications for neural networks developers/researchers and suggestions on future research areas are discussed.

Neural network applications in business: a review and analysis of the literature (1988-95)

The purpose of this research is to present a comprehensive bibliography of neural network application research in business. One hundred and twenty-seven neural network application papers and reports are identified through exhaustive literature searches. A classification of these articles by application area reveals that neural networks are being used for a diverse range of corporate functional activities, particularly in the areas of production/operations and finance. Information on the neural network development language/tool, the learning paradigm and the computer operating environment as reported in each article is included. Those journals which have published the most neural network business applications are also presented.

A bibliography of neural network business applications research: 1988–September 1994

In this work we design and compare different supervised learning algorithms to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The motivation for quick calculation of OPF cost outcomes stems from the growing need of algorithmic-based long-term and medium-term planning methodologies in power networks. Integrated in a multiple time-horizon coordination framework, we refer to this approximation module as a proxy for predicting short-term decision outcomes without the need of actual simulation and optimization of them. Our method enables fast approximate calculation of OPF cost with less than 1% error on average, achieved in run-times that are several orders of magnitude lower than of exact computation. Several test-cases such as IEEE-RTS96 are used to demonstrate the efficiency of our approach.

Supervised learning for optimal power flow as a real-time proxy

Artificial Neural Networks (ANNs) are often presented as powerful tools for data processing. Nevertheless, ANNs need a theory and consequently an epistemological foundation. Usually scholars in this field seem to believe that the brain theory is an implicit theory for ANNs. But, we still do not have a complete brain theory, and the brain model for ANNs has been only an “inspiration” model. Further, a theory has to explain a set of models or a set of phenomena internally. So, ANN lacks a theory. In this part of the book the author creates and presents a theory of ANNs. Three levels of different complexity are created, in order to simulate a generative path starting from the elementary units up to the more complex ones. The basic concepts of the first level are the node and the connections. At the second level the main concept is the network. At the last level the concept of an artificial organism is the key concept. Explicit rules govern the conversion from one level to another. Further, at each level, sem...

Theory: Foundations of Artificial Neural Networks

A neural-net approach to economic power dispatch

A distributed problem solving system for short-term load forecasting

Based on distributed artificial intelligence technology, this paper proposes a development tool for managing large distributed processing systems. This Cooperated Multi Agent Framework (CMAF) system is cornposited of a set of problem solving agents that autonomously execute and coordinately cooperate each other io employ the network management functions. The objectives of design are two major goals: one for problem solving paradigm, and the other for network management. To demonstrate its eflectness, a prototyping of the CMAF in the fault management is implemented and briefly described along with the results of preliminary experiments. It shows that the CMAF ogers advantages of performance, reliability, extensibility and the ability t o handle the incomplete data and knowledge. Furthermore, the versatile properties of reusability shell, transparency facility and adaptive knowledge bases an CMAF will be developed as tools for future large distributed application.

http://ieeexplore.ieee.org/iel3/5090/13882/00639820.pdf

Ronlon Tsai

Papers

A neural-net approach to economic power dispatch

A distributed problem solving system for short-term load forecasting

A Development Tool for Enterprise Management in Large Distributed Systems

Arranging the order of feature-extraction operations in pattern classification