{"title":"基于神经网络的激励动力契约参数评估方法","authors":"K. Wong, A. David","doi":"10.1109/ANN.1993.264335","DOIUrl":null,"url":null,"abstract":"This paper proposes a neural network approach to determining the contractual parameters of incentive power contracts. It describes the incentive power contract for a market in which the electricity supply industry has been largely privatized and suppliers compete to build plant and provide power supply. Since it is difficult to formulate and link practical decision factors such as management and technical factors with the parameters in terms of which a financial contract is usually formulated, neural networks appear to be a natural choice to solve the problem. A network is set up and trained to solve this problem and to work out contractual parameters.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A neural network approach to evaluate contractual parameters of incentive power contracts\",\"authors\":\"K. Wong, A. David\",\"doi\":\"10.1109/ANN.1993.264335\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a neural network approach to determining the contractual parameters of incentive power contracts. It describes the incentive power contract for a market in which the electricity supply industry has been largely privatized and suppliers compete to build plant and provide power supply. Since it is difficult to formulate and link practical decision factors such as management and technical factors with the parameters in terms of which a financial contract is usually formulated, neural networks appear to be a natural choice to solve the problem. A network is set up and trained to solve this problem and to work out contractual parameters.<<ETX>>\",\"PeriodicalId\":121897,\"journal\":{\"name\":\"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANN.1993.264335\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1993.264335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A neural network approach to evaluate contractual parameters of incentive power contracts
This paper proposes a neural network approach to determining the contractual parameters of incentive power contracts. It describes the incentive power contract for a market in which the electricity supply industry has been largely privatized and suppliers compete to build plant and provide power supply. Since it is difficult to formulate and link practical decision factors such as management and technical factors with the parameters in terms of which a financial contract is usually formulated, neural networks appear to be a natural choice to solve the problem. A network is set up and trained to solve this problem and to work out contractual parameters.<>