Download e-book for iPad: Applications of Soft Computing in Time Series Forecasting: by Pritpal Singh

By Pritpal Singh

ISBN-10: 3319262920

ISBN-13: 9783319262925

ISBN-10: 3319262939

ISBN-13: 9783319262932

This ebook reviews on an in-depth examine of fuzzy time sequence (FTS) modeling. It studies and summarizes prior examine paintings in FTS modeling and likewise presents a quick advent to different soft-computing options, resembling synthetic neural networks (ANNs), tough units (RS) and evolutionary computing (EC), concentrating on how those concepts could be built-in into various levels of the FTS modeling method. particularly, the publication describes novel tools as a result of the hybridization of FTS modeling techniques with neural networks and particle swarm optimization. It additionally demonstrates how a brand new ANN-based version could be effectively utilized within the context of predicting Indian summer season monsoon rainfall. due to its easy-to-read kind and the transparent factors of the versions, the e-book can be utilized as a concise but complete reference advisor to fuzzy time sequence modeling, and may be useful not just for graduate scholars, but in addition for researchers and execs operating for tutorial, enterprise and govt organizations.

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Extra resources for Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques

Example text

1) Here, each input node take the previous days (t −n), . . , Al , . . , Am , An ) to predict one day (t) advance time series value “A j ”. In Eq. 1, each “t” represent the day for considered fuzzified time series values. 2 RS: An Introduction RS is a new mathematical tool proposed by Pawlak (Pawlak 1982). The RS concept (Cheng et al. 2010) is based on the assumption that with every associated object of the universe of discourse, some information objects characterized by the same information are indiscernible in the view of the available information about them.

In this model, they suggested some improvement in Algorithm 2 as: (i) Introduction of union (∨) and intersection (∧) operators. This operators are applied in Step 8 of Algorithm 2. , and (ii) For defuzzification operation, they employ Principal 1 and Principal 2 (as discussed in Sect. 3) in Step 9 of Algorithm 2. • Bajestani and Zare (Bajestani and Zare 2011) model: This model is the enhancement of the model proposed by Huarng and Yu (2005). In this model, researchers employ the four changes as: (i) Using triangular fuzzy set with indeterminate legs and optimizing these triangular fuzzy sets.

2005). This Type2 fuzzy sets concept was first introduced by Zadeh (1975a) in 1975. In Type-1 fuzzy set, the degree of membership is characterized by a crisp value; whereas in Type-2 fuzzy set, the degree of membership is regarded as a fuzzy set (Chen 2012). Thus, if there are more uncertainty in the event, and we have difficulty in determining its exact value, then we simply use Type-1 fuzzy sets, rather than crisp sets. But, ideally we have to use some finite-type sets, just like Type-2 fuzzy sets (Mencattini et al.

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Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques by Pritpal Singh


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