In this paper, a neuro-fuzzy-based method for analysing odour generation factors to the\u00a0perception of livestock farm odour was proposed. The proposed approach incorporates\u00a0neuro-adaptive learning techniques into fuzzy logic method. Rather than choosing the\u00a0parameters associated with a given membership function by trail and error, these\u00a0parameters could be tuned automatically in a systematic manner so as to adjust the\u00a0membership functions of the input\/output variables for optimal system performance. A\u00a0multi-factor livestock farm odour model was developed, and both numeric factors and\u00a0linguistic factors were considered. The proposed approach was tested with a livestock farm\u00a0odour database. \u00a0It\u00a0can incorporate non-numeric data and\u00a0subjective human expert knowledge, which allows prior\u00a0knowledge to be included in the model. In addition, the\u00a0membership functions can be tuned during the learning\u00a0according to input\/output data to optimise performance. This\u00a0feature is important as it can avoid inappropriate predetermination\u00a0of membership functions, thus minimising errors\u00a0resulting from the limited knowledge of the livestock farm\u00a0odour system. A livestock farm database collected by our\u00a0research team has been used in this study. The results show\u00a0that the proposed approach is effective and provides a much\u00a0more accurate odour prediction in comparison with a typical\u00a0multi-layer feedforward neural network.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, a neuro-fuzzy-based method for analysing odour generation factors to the\u00a0perception of livestock farm odour was proposed. The proposed approach incorporates\u00a0neuro-adaptive learning techniques into fuzzy logic method. Rather than choosing the\u00a0parameters associated with a given membership function by trail and error, these\u00a0parameters could be tuned automatically in a systematic manner so as to […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18120,19],"tags":[46,27245],"class_list":["post-15099","post","type-post","status-publish","format-standard","hentry","category-ontario-pork","category-pork-insight-articles","tag-feed","tag-trail"],"_links":{"self":[{"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/posts\/15099","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/comments?post=15099"}],"version-history":[{"count":1,"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/posts\/15099\/revisions"}],"predecessor-version":[{"id":15101,"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/posts\/15099\/revisions\/15101"}],"wp:attachment":[{"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/media?parent=15099"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/categories?post=15099"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prairieswine.com\/rsc\/wp-json\/wp\/v2\/tags?post=15099"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}