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Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a...
In order to resolve the computational complexity for local map matching of hierarchical simultaneous localization and mapping (SLAM), a novel self-organizing fuzzy neural networks (SOFNN) based approach was proposed in this paper. The matching component for local maps in the hierarchical SLAM is realized by an SOFNN. A subset of signature elements included in a local map was chosen by a clustering...
Artificial Neural Networks have found a variety of applications that cover almost every domain. The increasing use of Artificial Neural Networks and machine learning has led to a huge amount of research and making in of large data sets that are used for training purposes. Handwriting recognition, speech recognition, speaker recognition, face recognition are some of the varied areas of applications...
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...
In this paper, we propose a fast C-means based training of Fuzzy Hopfield neural network and apply it to image segmentation. According to the other ways which usually take a long time, we define a fast method for image segmentation. We present a new objective function, and its minimization by Lyapunov energy function which is based on two dimensional fuzzy Hopfield neural network. This objective function...
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