Structure identification of fuzzy model
Fuzzy Sets and Systems
International Journal of Approximate Reasoning
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
Adaptive neuro fuzzy controller for adaptive compliant robotic gripper
Expert Systems with Applications: An International Journal
Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Intelligent rotational direction control of passive robotic joint with embedded sensors
Expert Systems with Applications: An International Journal
Development of optimal fuzzy models for predicting the strength of intact rocks
Computers & Geosciences
A neuro-fuzzy approach in the classification of students' academic performance
Computational Intelligence and Neuroscience
| Hi-index | 0.01 |
The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.