提出了一个结合融合空间约束的模糊C均值(Fuzzy C means with spatial constraints,FCMS)聚类与变分水平集的图像模糊聚类分割模型.在该模型中引入了一个基于图像局部信息和空间信息的外部模糊聚类能量,从而可以获取精确的局部图像的空...提出了一个结合融合空间约束的模糊C均值(Fuzzy C means with spatial constraints,FCMS)聚类与变分水平集的图像模糊聚类分割模型.在该模型中引入了一个基于图像局部信息和空间信息的外部模糊聚类能量,从而可以获取精确的局部图像的空间特征,使得本文模型对噪声图像的聚类分割具有较强的鲁棒性.采用不同类型的实验图像,将本文模型与10个不同类型的图像分割模型进行了对比实验,实验结果显示本文模型能克服图像中噪声影响并取得较满意的聚类分割结果.展开更多
This paper explores the power of stock trading trend using an integrated New ThresholdFuzzy Cognitive Maps(NTFCMs)Markov chain model.This new model captures thepositive as well as the negative jumps and predicts the t...This paper explores the power of stock trading trend using an integrated New ThresholdFuzzy Cognitive Maps(NTFCMs)Markov chain model.This new model captures thepositive as well as the negative jumps and predicts the trend for different stocks over 4months which follow an uptrend,downtrend and a mixed trend.The mean absolute percent error(MAPE)tolerance limits,the root mean square error(RMSE)tolerance limits aredetermined for various stock indices over a multi-timeframe period and observed for theexisting methods lying within the defined limits.The results show for every‘n’number ofpredictions made,the predicted close value of the day’s stock price was within tolerancelimit with 0%error and with 100%accuracy in predicting the future trend.展开更多
文摘提出了一个结合融合空间约束的模糊C均值(Fuzzy C means with spatial constraints,FCMS)聚类与变分水平集的图像模糊聚类分割模型.在该模型中引入了一个基于图像局部信息和空间信息的外部模糊聚类能量,从而可以获取精确的局部图像的空间特征,使得本文模型对噪声图像的聚类分割具有较强的鲁棒性.采用不同类型的实验图像,将本文模型与10个不同类型的图像分割模型进行了对比实验,实验结果显示本文模型能克服图像中噪声影响并取得较满意的聚类分割结果.
文摘This paper explores the power of stock trading trend using an integrated New ThresholdFuzzy Cognitive Maps(NTFCMs)Markov chain model.This new model captures thepositive as well as the negative jumps and predicts the trend for different stocks over 4months which follow an uptrend,downtrend and a mixed trend.The mean absolute percent error(MAPE)tolerance limits,the root mean square error(RMSE)tolerance limits aredetermined for various stock indices over a multi-timeframe period and observed for theexisting methods lying within the defined limits.The results show for every‘n’number ofpredictions made,the predicted close value of the day’s stock price was within tolerancelimit with 0%error and with 100%accuracy in predicting the future trend.
