scholarly journals Navigation Behaviors Based on Fuzzy ArtMap Neural Networks for Intelligent Autonomous Vehicles

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
Amine Chohra ◽  
Ouahiba Azouaoui
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Target Location ◽  
Fuzzy Artmap ◽  
Suggested Approach ◽  
Hybrid Intelligent Systems ◽  
Simplified Fuzzy Artmap ◽  
Recognition Learning

The use of hybrid intelligent systems (HISs) is necessary to bring the behavior of intelligent autonomous vehicles (IAVs) near the human one in recognition, learning, adaptation, generalization, decision making, and action. First, the necessity of HIS and some navigation approaches based on fuzzy ArtMap neural networks (FAMNNs) are discussed. Indeed, such approaches can provide IAV with more autonomy, intelligence, and real-time processing capabilities. Second, an FAMNN-based navigation approach is suggested. Indeed, this approach must provide vehicles with capability, after supervised fast stable learning: simplified fuzzy ArtMap (SFAM), to recognize both target-location and obstacle-avoidance situations using FAMNN1 and FAMNN2, respectively. Afterwards, the decision making and action consist of two association stages, carried out by reinforcement trial and error learning, and their coordination using NN3. Then, NN3 allows to decide among the five (05) actions to move towards 30∘, 60∘, 90∘, 120∘, and 150∘. Third, simulation results display the ability of the FAMNN-based approach to provide IAV with intelligent behaviors allowing to intelligently navigate in partially structured environments. Finally, a discussion, dealing with the suggested approach and how its robustness would be if implemented on real vehicle, is given.

Prediction of International Stock Markets Based on Hybrid Intelligent Systems

2016 ◽  
pp. 110-124
Author(s):  
Salim Lahmiri
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Stock Market ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Market Price ◽  
Hybrid Intelligent Systems ◽  
Out Of Sample ◽  
Artificial Neural Network Ann

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

scholarly journals Multimodal neural network classifier of the functional state of the respiratory system

2021 ◽  
Vol 2094 (3) ◽  
pp. 032064
Author(s):  
S A Filist ◽  
M B Myasnyankin ◽  
R I Safronov ◽  
A A Kuzmin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Functional State ◽  
Respiratory System ◽  
Intelligent Systems ◽  
Neural Network Classifier ◽  
Living Objects ◽  
Source Of Information

Abstract At present, the intelligent systems are used to study the functional state of living objects. They are often built on a multimodal basis, i.e. decision-making is carried out through several information channels with the subsequent aggregation of these decisions. In the proposed method, the electrocardiosignal is a source of information. The principle of multimodality is implemented by analyzing of various methods and aggregating the results using neural networks of block and hierarchical structure. The method was tested on the classification of patients with pneumonia with a clear diagnosis. A group of volunteers without pulmonary pathologies was used as an indifferent class.

Classification of Characters Using Multilayer Perceptron and Simplified Fuzzy ARTMAP Neural Networks

2017 ◽  
Vol 23 (6) ◽  
pp. 5151-5155
Author(s):  
N. M Wafi ◽  
Naseer Sabri ◽  
Shahrul Nizam Yaakob ◽  
A. S. A Nasir ◽  
A. R. A Nazren ◽  
...  
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Fuzzy Artmap ◽  
Simplified Fuzzy Artmap

Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making

2012 ◽  
pp. 444-466
Author(s):  
Amine Chohra ◽  
Nadia Kanaoui ◽  
Véronique Amarger ◽  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Industrial Applications ◽  
Suggested Approach ◽  
Intelligent Diagnosis

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

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