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Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement
Fault diagnosis of electronic systems using intelligent techniques: a review
TLDR
This paper reviews this research, primarily covering rule-based, model- based, and case-based approaches and applications, which may lead to a greater acceptance of automated diagnosis.
A Context-Based Word Indexing Model for Document Summarization
TLDR
A context sensitive document indexing model based on the Bernoulli model of randomness, using the lexical association between terms to give a context sensitive weight to the document terms has been proposed.
A Distributed Task Allocation Algorithm for a Multi-Robot System in Healthcare Facilities
TLDR
The proposed Consensus Based Parallel Auction and Execution (CBPAE), a distributed algorithm for task allocation in a system of multiple heterogeneous autonomous robots deployed in a healthcare facility, based on auction and consensus principles, is suitable for highly dynamic real world environments.
Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a Brain–Computer Interface
TLDR
It is shown that the modified SOFNN favorably compares to other evolving fuzzy systems in terms of accuracy and structural complexity and a fully parameterless BCI that lends itself well to autonomous adaptation is realizable.
Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection
TLDR
The evaluation experiments show that the proposed adaptive hidden Markov model with anomaly states (AHMMAS) model can effectively detect price manipulation patterns and outperforms the selected benchmark models.
Quantum Neural Network-Based EEG Filtering for a Brain–Computer Interface
TLDR
It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.
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