A 
Soft Computing (SC) optimizer for designing a 
Knowledge Base (KB) to be used in a 
control system for controlling a suspension 
system is described. The SC optimizer includes a 
fuzzy inference engine based on a 
Fuzzy Neural Network (FNN). The SC Optimizer provides 
Fuzzy Inference System (FIS) structure selection, FIS structure optimization 
method selection, and teaching 
signal selection and generation. The user selects a 
fuzzy model, including one or more of: the number of input and / or output variables; the type of 
fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A 
Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the 
fuzzy model, optimal linguistic variable parameters, and a teaching 
signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a 
fitness function based on a response of the actual suspension 
system model of the controlled suspension 
system. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.