{"title":"一种感知模糊神经模型","authors":"J. T. Rickard, J. Aisbett","doi":"10.1109/MCDM.2014.7007188","DOIUrl":null,"url":null,"abstract":"We introduce a fuzzy neural model which is more intuitive and general than the traditional weighted sum/squashing function neuron model. Positively and negatively causal inputs are separately aggregated using operators that are selected to suit the particular application. The aggregations are then combined using a simple arithmetic transformation. We outline the computational process when inputs and importance weights are vocabulary words modelled as interval type-2 fuzzy sets, and illustrate on predictions of gold price changes.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A perceptual fuzzy neural model\",\"authors\":\"J. T. Rickard, J. Aisbett\",\"doi\":\"10.1109/MCDM.2014.7007188\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a fuzzy neural model which is more intuitive and general than the traditional weighted sum/squashing function neuron model. Positively and negatively causal inputs are separately aggregated using operators that are selected to suit the particular application. The aggregations are then combined using a simple arithmetic transformation. We outline the computational process when inputs and importance weights are vocabulary words modelled as interval type-2 fuzzy sets, and illustrate on predictions of gold price changes.\",\"PeriodicalId\":335170,\"journal\":{\"name\":\"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MCDM.2014.7007188\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCDM.2014.7007188","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We introduce a fuzzy neural model which is more intuitive and general than the traditional weighted sum/squashing function neuron model. Positively and negatively causal inputs are separately aggregated using operators that are selected to suit the particular application. The aggregations are then combined using a simple arithmetic transformation. We outline the computational process when inputs and importance weights are vocabulary words modelled as interval type-2 fuzzy sets, and illustrate on predictions of gold price changes.
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