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In existing fuzzy time series forecasting models,the intuitionistic fuzzy relationship groups and deterministic transition rules excessively relied on scale of the training data.A long-term intuitionistic fuzzy time series (IFTS) fore-casting model based on DTW was proposed.The IFTS segment base was constructed by IFCM.The complexity of sys-tem was reduced by dynamic update and maintaining of the rule base.The computing method of IFTS segments similar-ity based on the distance of DTW was proposed,which was valid for matching unequal length time series segments.The proposed model implements on the synthetic and the temperature dataset,which including different time series patterns,respectively.The experiments illustrate that the forecasting accuracy of the proposed model is higher than the others on the different tendency patterns of time series.The proposed model overcomes the limitation of single time series pattern and improves the generalization ability.

In existing fuzzy time series forecasting models,the intuitionistic fuzzy relationship groups and deterministic transition rules excessively relied on scale of the training data.A long-term intuitionistic fuzzy time series (IFTS) fore-casting model based on DTW was proposed.The IFTS segment base was constructed by IFCM.The complexity of sys-tem was reduced by dynamic update and maintaining of the rule base.The computing method of IFTS segments similar-ity based on the distance of DTW was proposed,which was valid for matching unequal length time series segments.The proposed model implements on the synthetic and the temperature dataset,which including different time series patterns,respectively.The experiments illustrate that the forecasting accuracy of the proposed model is higher than the others on the different tendency patterns of time series.The proposed model overcomes the limitation of single time series pattern and improves the generalization ability.

A high order intuitionistic fuzzy time series forecasting model was built.In the new model,fuzzy clustering algorithm was used to get unequal intervals,and a more objective technique for ascertaining membership and non-membership functions of intuitionistic fuzzy set was proposed.On these bases,forecasting rules based on mul-ti-dimension intuitionistic fuzzy modus ponens inference were established.At last,contrast experiments on the enroll-ments of the university of Alabama and the daily average temperature of Beijing were carried out,which show that the novel model has a clear advantage of improving the forecasting accuracy.

A high order intuitionistic fuzzy time series forecasting model was built.In the new model,fuzzy clustering algorithm was used to get unequal intervals,and a more objective technique for ascertaining membership and non-membership functions of intuitionistic fuzzy set was proposed.On these bases,forecasting rules based on mul-ti-dimension intuitionistic fuzzy modus ponens inference were established.At last,contrast experiments on the enroll-ments of the university of Alabama and the daily average temperature of Beijing were carried out,which show that the novel model has a clear advantage of improving the forecasting accuracy.

Considering that the generalization of the learning machine performed poorly in the present intuitionistic fuzzy kernel matching pursuit algorithm(IFKMP)due to its training method and stopping criteria,a new recognition method based on intuitionistic fuzzy kernel matching pursuit ensemble(IFKMPE)was proposed by introducing the idea of ensemble learning.In IFKMPE,the double perturbation strategy including sample and parameter perturbation was applied to generate the sub-learning machine,the recognition results were fused by the principle of majority voting,and therefore both the classify accuracy and generation ability were enhanced.Simulation results show the new algorithm IFKMPE performs better in terms of recognition accuracy and stability of sample learning compared with the traditional ones.

Considering that the generalization of the learning machine performed poorly in the present intuitionistic fuzzy kernel matching pursuit algorithm(IFKMP)due to its training method and stopping criteria,a new recognition method based on intuitionistic fuzzy kernel matching pursuit ensemble(IFKMPE)was proposed by introducing the idea of ensemble learning.In IFKMPE,the double perturbation strategy including sample and parameter perturbation was applied to generate the sub-learning machine,the recognition results were fused by the principle of majority voting,and therefore both the classify accuracy and generation ability were enhanced.Simulation results show the new algorithm IFKMPE performs better in terms of recognition accuracy and stability of sample learning compared with the traditional ones.

Kernel matching pursuit requires every step of searching process be global optimal searching in the redundant dictionary of function.Namely,the dictionary learning time of KMP was too long.To the above drawbacks,a novel technique for KMP based on IFCM was proposed to substitute local searching for global searching by the property superiority of dynamic clustering performance,which was also the superiority in Intuitionistic fuzzy c-means algorithm.Then two testing including classification and effectiveness were carried out towards four real sample data.Subsequently,high resolution range profile (HRRP)was selected from the classical properties of target recognition in e middle ballistic trajectory,which were extracted for getting sub-range profile.Finally,three algorithms including FCM,KMP,IFCM-KMP were carried out respectively towards different kinds of sub-range profile samples in emulation platform,the conclusion of which fully demonstrates that the IFCM-KMP algorithm is superior over FCM and KMP when it comes to target recognition in the middle ballistic trajectory.

Kernel matching pursuit requires every step of searching process be global optimal searching in the redundant dictionary of function.Namely,the dictionary learning time of KMP was too long.To the above drawbacks,a novel technique for KMP based on IFCM was proposed to substitute local searching for global searching by the property superiority of dynamic clustering performance,which was also the superiority in Intuitionistic fuzzy c-means algorithm.Then two testing including classification and effectiveness were carried out towards four real sample data.Subsequently,high resolution range profile (HRRP)was selected from the classical properties of target recognition in e middle ballistic trajectory,which were extracted for getting sub-range profile.Finally,three algorithms including FCM,KMP,IFCM-KMP were carried out respectively towards different kinds of sub-range profile samples in emulation platform,the conclusion of which fully demonstrates that the IFCM-KMP algorithm is superior over FCM and KMP when it comes to target recognition in the middle ballistic trajectory.