Features: ·Up to 8 Input Variables and 4 Output Variables·Up to 8 Fuzzy Sets for each Input Variables·Up to 214 Fuzzy Rules·Rules Learning Phase using an unsupervised WTA-FAM·Membership Functions Learning Phase using a supervised BACK-FAM·Automatic and Manual Learning Rate·Rules Minimizer·Gaussian...
AFM10: Features: ·Up to 8 Input Variables and 4 Output Variables·Up to 8 Fuzzy Sets for each Input Variables·Up to 214 Fuzzy Rules·Rules Learning Phase using an unsupervised WTA-FAM·Membership Functions Le...
SeekIC Buyer Protection PLUS - newly updated for 2013!
268 Transactions
All payment methods are secure and covered by SeekIC Buyer Protection PLUS.
Adaptive Fuzzy Modeller (AFM) is a tool that easily allows to obtain a model of a system based on Fuzzy Logic data structure, starting from the sampling of a process/function expressed in terms of Input\Output values pairs (patterns). AFM10's primary capability is the automatic generation of a database containing the inference rules and the parameters describing the membership functions.
The generated Fuzzy Logic knowledge base represents an optimized approximation of the process/ function provided as input. The AFM has the capability to translate AFM10's project files to FUZZYSTUDIOÔ project files, MATLAB and C code, in order to use this environment as a support for simulation and control . The block diagram in fig.2 illustrates the AFM logic flow.