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We describe in this paper a comparative study of fuzzy inference systems as methods of integration in modular neural networks (MNNpsilas) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic...
The restricted structure of fuzzy grid type based partitioning commonly employed in fuzzy model is limiting the fuzzy model on the whole to accurately describe the underlying distribution of data points in feature space. Common solution via the use of more linguistic terms to finely describe the feature space would confute the whole idea of introducing approximate reasoning. This paper proposes the...
A novel approach for data mining of steam turbine based on neural network and genetic algorithm is brought forward, aimed at overcoming shortages of some current knowledge attaining methods. The historical fault data of steam turbine is processed with fuzzy and discrete method firstly, a multiplayer backpropagation neural network is structured secondly, the neural network is trained via teacherpsilas...
This paper aims at developing near optimal traffic signal control for multi-intersection in city. Fuzzy control is widely used in traffic signal control. For improving fuzzy controlpsilas adaptability in fluctuate states, a controller combined with neuro-fuzzy system and adaptive dynamic programming (ADP) is designed. This controller can be used for cooperative control of multi-intersection. The adaptive...
The paper describes the combination of two modeling strategies between the accelerated Levenberg-Marquardt algorithm (accelerated LMA) on neuro-fuzzy approach and fuzzy clustering algorithm C-Means that can be used to forecast financial stock market such as Jakarta Stock Indices (JCI) using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The accelerated...
As the total cost of healthcare continues to rise, computerized methods are sought to improve the overall efficiency and effectiveness of healthcare systems. In this application, the focus is on healthcare claim payment processing, which is a major component of administrative healthcare costs. Due to the complexity of healthcare data, current methods require a large amount of healthcare claim payment...
This study focuses on the design of a dynamic Petri recurrent-fuzzy-neural-network (DPRFNN) control for the path tracking of a nonholonomic mobile robot. In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic...
This paper proposes a neuro-fuzzy system with a reinforcement learning algorithm to realize speedy acquisition of optimal swarm behaviors. The proposed system is constructed with a part of input states classification by the fuzzy net and a part of optimal behavior learning network adopting the actor-critic method. The membership functions and fuzzy rules in the fuzzy net are adaptively formed online...
This paper investigates the possibility of a pseudo-online adaptive training schema for Mamdani-type neuro-fuzzy models that have robust linguistic interpretability. As such verbatim models are incapable of complex constructs available to Takagi-Sugeno-type neuro-fuzzy models, a heuristic approach is developed to allow the rule bases to adapt accordingly to fundamental shifts in the characteristics...
This paper aims at presenting different strategies for the construction of beta basis function (BBF) fuzzy neural network. These strategies lead to the determination of the network architecture by determining the structure of the hidden layer and parameters of its centers based on data structure. For that, we use self organizing maps (SOM) clustering to construct a mapped structure of the real training...
This paper presents a fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as modular adaptive resonance theory map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness...
Fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of unsupervised and supervised learning. The network first quantizes input and output feature maps using fuzzy sets. They are subsequently conjuncted to form antecedents and consequences, and associated to form fuzzy if-then rules. These...
In this paper a fuzzy logic approach to determine the relevance of each module in modular neural networks for images recognition is presented. The tests were made with Type-1 and Interval Type-2 Fuzzy Inference Systems, to compare the performance of the proposed approach. In both cases the fusion operator for the modules is the Sugeno Integral, and the estimated parameters are the fuzzy densities.
In this paper, we study impulsive fuzzy BAM neural networks. Criteria are obtained for exponential stability of globally exponential stability of periodic solution of time-varying delayed fuzzy neural networks with impulses.The criteria obtained in this paper is easily verifiable. It is believed that it is useful in design neural networks in practices.
Early detection of a tumorpsilas site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. Here, we apply a neural network clustering theory, Fuzzy ART, to generate the division of cancer samples, which is useful...
In this paper, a multi-sensor data fusion method based on dynamic fuzzy neural network (DFNN) for object recognition is proposed.DFNN is composed of two individual fuzzy neural networks. During the practical recognition process, one fuzzy neural network is used for recognition while the other is tracking trained. At the appropriate time the role of the two networks can be exchanged according to certain...
Adaptive resonance theory (ART) is an unsupervised neural network. Fuzzy ART (FART) is a variation of ART, allows both binary and continuous input patterns. However, fuzzy ART has the category proliferation problem. In this study, to solve this problem, we propose a new fuzzy ART algorithm: fuzzy ART combining overlapped category in consideration of connections (C-FART). C-FART has two important features...
This paper presents an investigation of the influence of the RePART (Reward and Punishment ARTmap) neural network in structures of ensembles designed by three variants of boosting: Aggressive, Conservative and Inverse Boosting. In this investigation, it is aimed to analyze whether the use of this model is positive for ARTMAP-based ensembles. In addition, it aims to define which boosting strategy is...
Automatic pattern classifiers that allow for incremental learning can adapt internal class models efficiently in response to new information, without having to retrain from the start using all the cumulative training data. In this paper, the performance of two such classifiers - the fuzzy ARTMAP and Gaussian ARTMAP neural networks - are characterize and compared for supervised incremental learning...
Localist networks and especially neuro-fuzzy systems constitute promising techniques for data comprehension, but generally exhibit poor system interpretability and generalization ability. This paper aims at addressing the issues through a novel localist reduced fuzzy cerebellar model articulation controller (RFCMAC), that models the two-stage development of cortical memories in the human brain to...
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