How many layers are in neuro fuzzy inference?
Answers from top 9 papers
More filters
| Papers (9) | Insight |
|---|---|
38 Citations | The results of our initial experiments demonstrate a clear advantage of the adaptive neuro-fuzzy inference system genetic algorithm over the other techniques. |
01 Sep 2015 11 Citations | The results of experimental modelling confirm the effectiveness and computational simplicity of the proposed approach in comparison with conventional neuro-fuzzy systems. |
250 Citations | It is shown that neuro-fuzzy methods give better results than other, more conventional, modeling approaches. |
| The learning procedure is based on a gradient descent technique and the proposed model is general enough to be applied to other neuro-fuzzy architectures. | |
02 Dec 2001 14 Citations | This paper shows that neuro-fuzzy networks lend themselves well to nonlinear black-box modeling and prediction. |
01 Oct 2007 141 Citations | The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. |
01 Jan 2000 23 Citations | By applying this methodology to a great variety of neuro-fuzzy systems, it is possible to obtain general results about the most relevant factors defining the neural network design. |
55 Citations | The performance of this neuro-fuzzy model with relatively few input variables holds promise for modeling sites where the volume of available data is limited. |
| Experimental results show an advantage of the neuro-fuzzy models against classical statistical methods, although there is still room for improvement. |
Related Questions
What are the layers of CNN?4 answersThe layers of a Convolutional Neural Network (CNN) typically include the convolutional layer, pooling layer, and fully connected layer. The convolutional layer applies kernel filters to extract fundamental features from the input image. The pooling layer reduces the dimension of the feature maps, making the network more robust and invariant to small shifts and variations. The fully connected layer connects all neurons from the previous layer to the output layer, enabling object classification. Additionally, CNN architectures may also include other layers such as the activation layer, which defines the output of the neural network, and the batch normalization layer, which controls input distortion. Overall, CNNs are designed to automatically learn features from images and have been widely used in various applications such as classification, segmentation, and natural language processing.
What is the fuzzy ranking methods?4 answersFuzzy ranking methods are techniques used to rank fuzzy numbers based on their ambiguous values. These methods are important in various fields such as decision-making and multi-criteria analysis. Several approaches have been proposed to address the challenges of ranking fuzzy numbers. One approach is the use of statistical techniques like multi-dimensional scaling and principal component analysis to rank triangular and trapezoidal fuzzy numbers. Another approach involves the use of ranking functions to convert fuzzy values into crisp values, allowing for easier comparison and ranking. Additionally, methods like Haar ranking and Yager's ranking have been proposed for ranking intuitionistic dense fuzzy sets. These ranking methods aim to provide accurate rankings while considering the complexity and computational load of the calculations.
Fuzzy logic categorized as machine learning or deep learning?5 answersFuzzy logic is categorized as a part of machine learning and deep learning. It has been used in conjunction with neural networks to enhance deep learning performance. Fuzzy systems have also been explored in the field of artificial intelligence, specifically in neuro-fuzzy networks that establish a link between symbolic and connectionist schools of AI. Additionally, new architectures inspired by fuzzy logic have been proposed to address the flawed generalization of deep neural networks, resulting in models that are more local, better at rejecting noise samples, and more robust against adversarial examples. Fuzzy machine learning, which utilizes formal concepts and mathematical tools from fuzzy sets and fuzzy logic, is an area of research that combines fuzzy logic with machine learning techniques. Fuzzy logic has also been applied in the GARIC architecture, which combines fuzzy reinforcement learning with fuzzy logic control for various applications, including aerospace systems and consumer products.
What is Fuzzy SVM?4 answersFuzzy SVM is a classification model that combines fuzzy logic and support vector machines (SVM). It is used to handle uncertain and imbalanced data in machine learning tasks. Fuzzy SVM assigns fuzzy membership values to data samples, increasing the certainty of uncertain data. This is done in both the input space and feature space, with higher fuzzy membership assigned to minority samples compared to majority samples. By incorporating fuzzy logic, Fuzzy SVM improves the accuracy of traditional SVM models. It is particularly useful in applications such as cloth pattern recognition, where there are large intra-pattern variations. Fuzzy SVM has been shown to outperform traditional SVM-based methods in various experiments.
How does fuzzy neural inference work?9 answers
What are the main steps in fuzzy inference system?9 answers