There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential e...

Artificial ventilation is a crucial supporting treatment for Intensive Care Unit. However, as the ventilator control becomes increasingly more complex, it is non-trivial for less experienced clinicians to control the settings. In this paper, the novel Hebbian based Rule Reduction (HeRR) neuro-fuzzy system is applied to model this control problem for intra-patient and inter-patient ventilator co...

the functions employed in an estimation of costly measured soil properties from either widely available or more easily obtained basic soil properties are referred to as pedotransfer functions. to develop pedotransfer functions, one can use multivariate regression, neural networks and neuro-fuzzy models. to make a comparison among the mentioned models, 153 soil samples were collected from soils ...

Performance of a fuzzy system identifier is investigated against a fossil fuel boiler data. A multi-layer neuro-ftrzzy system presents identification of a drum type boiler. This identification techruque provides a rule-based approach to express the boiler dynamics in fuzzy rules that are generated from the experimental boiler data. The interconnections of neuro-fuzzy layers furnish these fuzzy ...

A neuro-fuzzy system is the combined the advance feature of fuzzy logic and neural network, it is simply a fuzzy inference system that is trained by the learning concept of neural network. In NFS learning mechanism fine-tunes the underlying fuzzy inference system. This paper presents fundamental concepts and parameterized comparison in the aspects of fuzzy logic, neural network and neuro-fuzzy ...

The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid intelligent methods for mining the data. This focus is required even more in the context of increasing number of different classifiers applied in toxicity prediction. Consequently, there exist a need to develop tools to integrate various approaches. The goal of this research is to apply ...

This paper outlines all the computational methods which have been applied to the conflict management. A survey of all the pertinent literature relating to conflict management is also presented. The paper then introduces the Takagi-Sugeno fuzzy model for the analysis of interstate conflict. It is found that using interstate variables as inputs, the Takagi-Sugeno fuzzy model is able to forecast c...

Neuro-fuzzy networks revealed their proficiency in learning from data, while offering a transparent and somehow interpretable rule-based model. Recent research focused either on the interpretability of the chosen model or on the system performance. Regarding the interpretability, here an index to control the trade-off between complexity and performance, some insights into fuzzy partitions prope...

The shapes of if-part fuzzy sets affect the approximating capability of fuzzy systems. In this paper, the fuzzy systems with the kernel-shaped if-part fuzzy sets are built directly from the training data. It is proved that these fuzzy systems are universal approximators and their uniform approximation rates can be estimated in the single-input–single-output (SISO) case. On the basis of these ra...