In this paper, an experimental evaluation of the neurofuzzy models NEFCLASS and FuNN is conducted in real world pattern recognition applications. The models are investigated with respect to classification performance and the number of rules generated and compared to the traditional MLP network trained with backpropagation. The models NEFCLASS and FuNN are examined in benchmarking problems from ...

Simple power electronics and fault tolerance are advantages of SRM drives. However, excessive torque ripple has limited their application. This paper presents a novel method of controlling the motor currents to minimise the torque ripple based on a neurofuzzy compensator. In the proposed controller, a compensating signal is added to the output of a PI controller, in a current-regulated speed co...

A neural network can approximate a function, but it is impossible to interpret the result in terms of natural language. The consolidation of neural networks and fuzzy logic in neurofuzzy models provides learning as well as readability. This paper aims at modeling the input-output relationship with fuzzy IF-THEN rules by using fuzzy clustering technique. The main difference between fuzzy cluster...

The integration of neural networks and fuzzy inference systems could be formulated into three main categories: cooperative, concurrent and integrated neuro-fuzzy models. We present three different types of cooperative neuro-fuzzy models namely fuzzy associative memories, fuzzy rule extraction using self-organizing maps and systems capable of learning fuzzy set parameters. Different Mamdani and ...

Understanding data is one of most important problems. Popular crisp logic rules are easy to understand and compare, however for some datasets the number of extracted rules is very large, what affect reduction of generalization and makes the system less transparent. Another solution are fuzzy logic rules, which are much more flexible, however they don’t support symbolic and nominal attributes. A...

Simple power electronic drive circuit and fault tolerance of converter are specific advantages of SRM drives, but excessive torque ripple has limited its application. This paper presents a novel method of controlling the motor currents to minimize the torque ripple, using a neuro-fuzzy compensator. In the proposed control concept, a compensating signal is added to the output of a classical PI c...

An efficient first grasp for a wheelchair robotic arm-hand with pressure sensing is determined and presented. The grasp is learned by combining the advantages of neural networks and fuzzy logic into a hybrid control algorithm which learns from its tip and slip control experiences. Neurofuzzy modifications are outlined, and basic steps are demonstrated in preparation for physical implementation....

The paper describes a Neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neurofuzzy network efficiently. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared ...

We present a formal description of a neurofuzzy system capable of aligning two sequences recognizing their internal structure. The alignment is done on two levels: grouping of the elements and alignment of groups. The system incorporates expert knowledge on both levels. Furthermore, it can be optimized by a learning algorithm adapted from the neural network domain. The system has been applied t...

This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fu...