142 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 7 George Thomas Cleveland State University, USA Timothy Wilmot Cleveland State University, USA Steve Szatmary Cleveland State University, USA Dan Simon Cleveland State University, USA Evolutionary Optimization of Artifcial Neural Networks for Prosthetic Knee Control ABSTRACT This chapter discusses closed-loop control development and simulation results for a semi-active above- knee prosthesis. This closed-loop control is a delta control that is added to previously developed open- loop control. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. Closed-loop control using artifcial neural networks (ANNs) are developed, which is an intelligent control method. The ANNs are trained with biogeography- based optimization (BBO), which is a recently developed evolutionary algorithm. This research contrib- utes to the feld of evolutionary algorithms by demonstrating that BBO is successful at fnding optimal solutions to real-world, nonlinear, time varying control problems. The research contributes to the feld of prosthetics by showing that it is possible to fnd efective closed-loop control signals for a newly proposed semi-active hydraulic knee prosthesis. The research also contributes to the feld of ANNs; it shows that they are able to mitigate some of the efects of noise and disturbances that will be common in normal operation of a prosthesis and that they can provide better robustness and safer operation with less risk of stumbles and falls. It is demonstrated that ANNs are able to improve average performance over open-loop control by up to 8% and that they show the greatest improvement in performance when there is high risk of stumbles. William Smith Cleveland Clinic, USA DOI: 10.4018/978-1-4666-3942-3.ch007