dvantages of Fuzzy Logic• Intuitiveness, simplicity, easy implementation, minimal knowledge of sys-

tem dynamics• Relates input to output in easily understood linguistic terms• Rules may be framed without precise definition of the system dynamics.• Easier to design• Increased robustness• Simplified knowledge acquisition and representation• No need of mathematical model. Fuzzy logic models the decision maker,

not the process.• Performance is perfected through simulation and experience.• Since fuzzy logic tolerates imprecision, sensor feedback is less critical. and

noise and component variations are tolerated.• Fuzzy controllers are inherently robust because their decisions include con-

tributions from dozens of rules. If a few rules "misfire" because of a badsensor or electrical component, other rules keep the output smooth andcontinuous.

• Advanced mathematics is not required.• Fuzzy controllers can be easily upgraded or modified by adding new rules

and membership functions. New rules can also reflect new control priori-ties, such as energy savings and quiet operation modes.

Disadvantages of Fuzzy Logic• Hard to develop a model from fuzzy system• It is sometimes difficult to determine the set of rules and membership func-

tions for complex systems.

• With more rules, the amount of data to be processed increases, resulting inmemory overload and longer computational time.

• Requires more fine-tuning• Fuzzy systems have a stigma ofjuzzy associated with them. Engineers give

less credit to fuzzy control and fuzzy decision-making, presuming that it isless accurate.

Advantages of ANN• Good fit for nonlinear models• Ability to adapt, learn, generalise, extrapolate; ability to operate in high-

noise environment; ease of maintenance• Fault and uncertainty tolerance is good• Automated knowledge and acquisition is good

Disadvantages of ANN• Needs lots of training data sets

• Needs lots of time and CPU power for training

• Unpredictable for utilization in untrained areas

• Accuracy of the system may diminish over time, so periodic trimmingrequired using expanded data sets

• ANNs are not well-understood, and therefore not widely accepted

• Not suited for mathematically accurate and precise applications

Feature Fuzzy Systems Neural Networks

Knowledge acquisition Human experts Numerical dataTraining method Precise definition needed Learning by example

Knowledge Easily verifiable Velification/modificationrepresentation not possible

Reasoning Heuristic, multivalued Algorithmic, parallelLinguistic interface Well-defined None

Fault tolerance High Very highRobustness Very high Very high

Both neural networks and fuzzy logic are structurally different, but they comple-ment each other as far as their strengths and weaknesses are concerned. Thaobjective of introducing fuzzy operations within neural network is to improve th~expressiveness and flexibility of the neural networks, and the objective of com-bining neural learning capabilities into fuzzy system is to make the fuzzy syste -capable of adaptation.

Hybrid systems like neuro-fuzzy, fuzzy expert, neural expert, GAs (Gene'Algorithms) + ANN are becoming popular. Suppose the process knowledge ~poor, and we only have a gigantic data set about the process. In such casPS..ANNS can be trained to extract the rules and learn membership functions fror::domain data, resulting in neuro-fuzzy systems.

In expert systems, reasoning and decision processes within an uncertain emi-ronment are emulated and manipulated through fuzzy tools, resulting in fuzzyexpert systems.

When domain knowledge is too complex or no human expertise is availabl .but training data is there; then ANN may be trained to function as expert system.resulting in neural expert system.

GAs can be used to provide a good set of initial weights and to fully train theANN or find the optimal network architecture. GAs in fuzzy domain can be usedto find optimal number of membership functions and optimal set of rules.