2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/80173We present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural freedom going beyond existing multi-expert models and an integrative formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learning.) Its uniform representation aims at a simple genetic encoding and evolutionary structure optimization of multi-expert systems. This paper contains a detailed description of the model and learning rules, empirically validates its functionality, and discusses future perspectives.LaTeX, 8 pages, 5 figuresAdaptation and Self-Organizing SystemsDisordered Systems and Neural NetworksNeural and Evolutionary Computingtext