This sounds really strange. Hopfield networks are almost 40-year old stuff. Your question sounds like "How could I develop an abacus for solving nonlinear differential equations?" or "How could I develop a three-blade propeller for the propulsion of a spacecraft launch vehicle?". Hopfield nets were a great new concept and a driving force in the early development of machine learning in the 1980s; they provided some insight into models of nervous systems, they were widely studied as elegant, complex, dynamic systems. But they turned out to be hopelessly inefficient for any kind of real-life application when compared to standard classifiers. The reasons for that are very well known and documented. The Hopfield net papers that I co-authored in the years 1984 to 1989 still generate a modest trickle of citations, but they are mainly from mathematicians interested in dynamic systems from a theoretical point of view. If your purpose is to perform real automatic classification of real faults in real electric power systems, by an efficient and competitive method, you should use a real classifier: multilayer Perceptron, Support vector machine, classification tree, Bayesian classifier, deep learning network if you have a huge database, or other. Just forget about Hopfield nets if you want to produce really applicable present-day science or technology.
Thank you very much for the answers, since I am trying for a hybrid algorithm by combining meta-heuristic and Hopfield Neural Network. Therefore, I am looking for the development of Hopfield as a classifier. I have made this hybrid combination for the optimization problem. But, now facing difficulties to develop the code for classifiers.@ Gérard Dreyfus, @Indranath Chatterjee