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A variety of fuzzy clustering methods exploit the Euclidean distance to quantify resemblance between data points. This distance is effective for revealing spherical clusters, but it does not perform well for data exhibiting more complicated geometry. So, in this paper, we present a new algorithm IFPCM with Minkowski distance applied on real and artificial datasets being generated according to various...
Feature selection is a very important technique in machine learning and pattern classification. Feature selection studies using batch learning methods are inefficient when handling big data in real world, especially when data arrives sequentially. Online Feature Selection is a new paradigm which is more efficient than batch feature selection methods but it still very challenging in large-scale ultra-high...
In this paper we proposed a writer adaptation system based on an adaptation module that is a plug-in for any writer-independent handwriting recognition systems. The adaptation module is a radial basis function neural network (RBF-NN) that is built using an incremental learning algorithm named GALTM-AM algorithm (Growing-Adjustment with Long-Term Memory). GALTM-AM train a new given data with some LTM...
Echo state networks (ESNs) fulfill considerable promises for topology fine-tuning in supervised training. However the randomness of the setting of ESN weights initialization affects badly the learning performance. On the other side, Particle Swarm Optimization (PSO) has proven its efficiency as an optimization tool to puzzle out optimal solutions in complex space. In this work, we present an ESN architecture...
In this paper we introduce the Interval type-2 Beta fuzzy set as a membership function in a Fuzzy Logic System (FLS). First order derivatives of type-1 and type-2 Beta functions were developed for designing fuzzy logic systems based on given input-output pairs. Then, the steepest descent algorithm is used to train Beta fuzzy basis functions to obtain the final fuzzy system. The performance of the...
In this paper, we apply the Beta Basis Function Neural Network (BBFNN) trained with cuckoo search (CS) for time series predictions. The cuckoo search algorithm optimizes the network parameters. In order to evaluate the effectiveness of the proposed method, we have carried out some experiments on four data sets: Mackey Glass, Lorenz attractor, Henon map and Box-Jenkins. We give also simulation examples...
Scintigraphic images are often characterized with much noise and a badly contrasted resolution which makes the perception of regions of interest very difficult. The renal quantification is how to define the regions of interests whose activities informs on the status of the renal function. In this context, the current study presents an intelligent system for the segmentation of renal regions in order...
In this paper, a new hybrid learning algorithm based on the global optimization techniques, is introduced to evolve the Flexible Beta Basis Function Neural Tree (FBBFNT). The structure is developed using the Extended Immune Programming (EIP) and the Beta parameters and connected weights are optimized using the Opposite-based Particle Swarm Optimization (OPSO) algorithm. The performance of the proposed...
This paper describes a clustering process taking inspiration from the cemetery organization of ants. The goal of this paper is (i) to show the importance of the local interactions which allow to produces complex and emergent behavior. (ii) To propose a multi-robot systems in the field of clustering objects allowing optimization of: time of convergence, rate of occupation of the objects in the environment...
The aim of this work is to present a particular design technique of hierarchical fuzzy controllers. The method makes an easy way to control complex systems with an automatic low complicated design. It was based on the systems capability to generate output informations when exited outside any control instance. This fact helps constructing input-output learning and testing data. This type of fuzzy controller...
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