Background: In this research, the control algorithms has been developed and
proposed based on a dynamic fuzzy neural network which is tolerant to the faults
of matrix calculations. The aim of this control algorithm is speed learning and more
compact network structures and its high generalizable performance, improves the
quality of the control. Thus, its tolerance to fault is of particular importance.
Aim: The main aim of this research is to present an algorithm that implements faulttolerant dynamic fuzzy neural network (DFNN). This control algorithm, inspired
by the direct inverse control method, uses the DFNN learning algorithm as an
estimator and controller, which makes the system tolerant to faults and improves
the control performance. The DFNN estimator and controller has been built on the
basis of extended RBF, and it performs as a TSK system, which results in speed
learning and a more compact network structure with high approximation and
generalization performance, and is tolerant to damage caused by fault with a fault
detection algorithm through data redundancy. In other words, it examines
algorithm base fault tolerance (ABFT), which is a low-cost, high-performance
scheme to detect and correct permanent and temporary errors in matrix
operations. In the end, a non-linear process will be investigated in this approach
and its efficiency can be observed in performing control operations. Each algorithm
has its own particular execution time, which is one of the important issues of
algorithm design, and its efficiency are checked according to its execution
time. The execution time of the fault-tolerant algorithm is different with and
without the fault and the execution time and efficiency of both are investigated
during performing of the algorithms.
Methodology: According to the DFNN learning algorithm, RBF units are first
determined by entering the training data. To do this, criteria of neuron generation
for hierarchical learning and factor of neuron generation, in