Book ChapterDOI
Adaptive Neuro Fuzzy Inference System Based Obstacle Avoidance System for Autonomous Vehicle
M. Karthikeyan,S. Sathiamoorthy,M. Vasudevan +2 more
- pp 118-126
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TLDR
A Nonlinear ANFIS algorithm to track the distance between the autonomous vehicle and the obstacle while vehicle is moving and the brake force required is proposed and the results are captured in this paper.Abstract:
Adaptive Neuro Fuzzy Inference System (ANFIS) is a well proven technology for predicting the output based on the set of inputs. ANFIS is predominantly used to track the set of inputs and output in order to achieve the target. In this paper, authors have proposed a Nonlinear ANFIS algorithm to track the distance between the autonomous vehicle and the obstacle while vehicle is moving and the brake force required. By tuning neuro fuzzy algorithm, accurate brake force requirement has been achieved and the results are captured in this paper. Back propagation algorithm based neural network & Sugeno model based Fuzzy inference system have been used in the proposed technique. Matlab/Simulink software platform is used to implement the proposed algorithm and proven the expected results.read more
Citations
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Proceedings ArticleDOI
Co-occurrence of Edges and Valleys with Support Vector Machine for Content Based Image retrieval
TL;DR: In this paper, co-occurrences of BDIP (block difference of inverse probabilities) is employed as a shape feature which extracts edges with valleys more effectively, and SVM is employed in the classification phase to significantly increase the accuracy and decrease the time cost of the proposed system.
Journal ArticleDOI
Control of an Autonomous Vehicle With Obstacles Identification and Collision Avoidance Using Multi View Convolutional Neural Network
M Karthikeyan,S Sathiamoorthy +1 more
TL;DR: Control of autonomous passenger vehicle using deep multi view convolutional neural network (CNN) for the identification of obstacles with 3 dimensional images of the same using Winograd Minimal filter algorithm (WMFA) has been presented and training of neural networks with multi view topology using Matlab/Simulink coding has been present with the results.
References
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Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
A System for Semi-Autonomous Tractor Operations
TL;DR: A system for tractor automation that stops for obstacles in the path of the machine, stopping for such obstacles until it receives advice from a supervisor over a wireless link, and self-monitoring to determine when human intervention is required is presented.
Journal ArticleDOI
Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River
TL;DR: In this article, the adaptive neuro-fuzzy inference system (ANFIS) was used to estimate the biochemical oxygen demand (BOD) of Surma River of Bangladesh, and the performance of the ANFIS models was assessed through the correlation coefficient (R ), mean squared error (MSE), mean absolute error (MAE), and Nash model efficiency (E ).
Journal ArticleDOI
Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping
Seyed Vahid Razavi Termeh,Khabat Khosravi,Majid Sartaj,Saskia Keesstra,Saskia Keesstra,Frank T.-C. Tsai,R. Dijksma,Binh Thai Pham +7 more
TL;DR: In this paper, an adaptive neuro-fuzzy inference system (ANFIS) using three meta-heuristic optimization algorithms (genetic algorithm, biogeography-based optimization (BBO), and simulated annealing (SA)) was applied to the Booshehr plain, Iran.
Design of Obstacle Avoidance System for Mobile Robot using Fuzzy Logic Systems
Xi Li,Byung-Jae Choi +1 more
TL;DR: A fuzzy logic system is designed and an obstacle avoidance algorithm for a path planning in unknown environment for a mobile robot and another new rule table is induced from the consideration of the distance to obstacles and the angle between the robot and the goal is proposed.