Differences:3. Application44. How they representand storethose samples5. How they associativelyinferenceFig.2 Function f mapsdomains X to range Y
Fig.2 Function f maps domains X to range Y 3. Application 4. How they represent and store those samples 5. How they associatively inference Differences:
Neural vs. fuzzy representation ofstructured knowledge Neural networkproblems:1. computational burden of training2. system inscrutabilityThere is no natural inferential audit tail, likean computational black box3. sample generation
Neural vs. fuzzy representation of structured knowledge ◼ Neural network problems: 1. computational burden of training 2. system inscrutability There is no natural inferential audit tail, like an computational black box. 3. sample generation
Neural vs. fuzzy representation ofstructured knowledgeFuzzy systems1. directly encode the linguistic sample(HEAVY,LONGER) in a matrix2. combine the numerical approaches with thesymbolic oneFuzzy approach does not abandon neural-networkit limits them to unstructured parameter and stateestimate, pattern recognition and cluster formation
Neural vs. fuzzy representation of structured knowledge ◼ Fuzzy systems 1. directly encode the linguistic sample (HEAVY,LONGER) in a matrix 2. combine the numerical approaches with the symbolic one ◼ Fuzzy approach does not abandon neural-network, it limits them to unstructured parameter and state estimate, pattern recognition and cluster formation