In order to tackle high dimensional, complex problems, learning models have go deeper. this article, a novel multilayer ensemble model with first-order evolving fuzzy systems as its building blocks is introduced. The proposed approach can effectively learn from streaming data on sample-by-sample basis and self-organizes multilayered system structure meta-parameters in feedforward, noniterative ...

due to the explosive growth of the world-wide web, automatictext summarization has become an essential tool for web users. in this paperwe present a novel approach for creating text summaries. using fuzzy logicand word-net, our model extracts the most relevant sentences from an originaldocument. the approach utilizes fuzzy measures and inference on theextracted textual information from the docu...

The Mamdani and TSK fuzzy models are fuzzy inference engines which have been most widely applied in real-world problems. Compared to the Mamdani approach, the TSK approach is more convenient when the crisp outputs are required. Common to both approaches, when a given observation does not overlap with any rule antecedent in the rule base (which usually termed as a sparse rule base), no rule can ...

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...

Background and Objectives: One of the problems in juice membrane clarification is the accumulation and deposition of rejected compounds on membrane surfaces or inside its pores which results in a membrane fouling. Materials and Methods: Several parameters can have influence on fouling in one hand and prediction of juice permeates flux during the membrane processing is of importance in indust...

Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements imprecision, and vagueness. However, there is another type of vagueness that arises when data have multiple forms of expression that fuzzy logic does not address quite well. This is the case for multiple instance learning problems (MIL). In MIL, an object is represented by a collection of instances, called a bag. A bag ...

This paper presents the design of a VLSI fuzzy processor, which is capable of dealing with complex fuzzy inference systems, i.e., fuzzy inferences that include rule chaining. The architecture of the processor is based on a computational model whose main features are: the capability to cope effectively with complex fuzzy inference systems; a detection phase of the rule with a positive degree of ...