14. The computer software product according to claim 11 , further comprising: an eleventh sequence of instructions which, when executed by the processor, causes said processor to characterize a crisp EE position and velocity utilizing a 4-input, first neural layer; a twelfth sequence of instructions which, when executed by the processor, causes said processor to perform fuzzification of said crisp inputs into linguistic variables through Gaussian transfer functions utilizing a second neural layer; a thirteenth sequence of instructions which, when executed by the processor, causes said processor to apply a product T-norm to produce firing strengths of each rule utilizing a third rule defining neural layer; a fourteenth sequence of instructions which, when executed by the processor, causes said processor to normalize firing strengths by calculating a ratio of each rule's firing strength to a sum of all rules' firing strengths, said normalizing firing strengths occurring in a fourth neural layer; a fifteenth sequence of instructions which, when executed by the processor, causes said processor to invert results of said firing strength normalizing step in a fifth neural layer; and a sixteenth sequence of instructions which, when executed by the processor, causes said processor to aggregate and defuzzify results from said the inversion in the fifteenth sequence, said aggregating and defuzzifying occurring in a sixth neural layer providing crisp outputs comprised of said joint torques and said sampling periods.
14. The computer software product according to claim 11 , further comprising: an eleventh sequence of instructions which, when executed by the processor, causes said processor to characterize a crisp EE position and velocity utilizing a 4-input, first neural layer; a twelfth sequence of instructions which, when executed by the processor, causes said processor to perform fuzzification of said crisp inputs into linguistic variables through Gaussian transfer functions utilizing a second neural layer; a thirteenth sequence of instructions which, when executed by the processor, causes said processor to apply a product T-norm to produce firing strengths of each rule utilizing a third rule defining neural layer; a fourteenth sequence of instructions which, when executed by the processor, causes said processor to normalize firing strengths by calculating a ratio of each rule's firing strength to a sum of all rules' firing strengths, said normalizing firing strengths occurring in a fourth neural layer; a fifteenth sequence of instructions which, when executed by the processor, causes said processor to invert results of said firing strength normalizing step in a fifth neural layer; and a sixteenth sequence of instructions which, when executed by the processor, causes said processor to aggregate and defuzzify results from said the inversion in the fifteenth sequence, said aggregating and defuzzifying occurring in a sixth neural layer providing crisp outputs comprised of said joint torques and said sampling periods. 15. The computer software product according to claim 14 , further comprising: a seventeenth sequence of instructions which, when executed by the processor, causes said processor to define said Gaussian transfer functions by the relation: μ A t j ⁡ ( x t ) = exp ⁢ ⁢ - 1 2 ⁢ { ( x t - a t j ) 2 ( b t j ) 2 } , t = 1 , … ⁢ , 4 , j = 1 , … ⁢ , J , wherein a i j and b i j are a mean and standard deviation of the j th membership function of the input variable x t , and c t j are fuzzy sets defining the consequence of the j th rule, such that: 
 c t j =f −1 (μ A t h ( x t )), f: →[ 0,1 ],t= 1, . . . ,3 ,j= 1 , . . . ,J wherein f is a continuous strong monotone function defined as: f ⁡ ( z ) = 1 1 + ⅇ - ( ρ ⁢ ⁢ z - σ ) wherein ρ and σ are real numbers affecting the position and slope of the inflection point of f.