Abstract: A neural network-based rating system includes a data set, said data set further comprising at least two records and at least one field associated with said records and a data rating application, which includes means for user input of ratings for at least a first of said records of said data set; at least one artificial neural network; means for automatically dimensioning said artificial neural network as a function of said fields within said data set; means for initiating training of said artificial neural network, said trained artificial neural network operative to generate ratings for at least a second of said records of said data set; means for initiating rating of at least said second record of said data set by said trained artificial neural network; and means for sorting said data set based on said user ratings and said artificial neural network-generated ratings.

Abstract: A discovery system employing a neural network, training within this system, that is stimulated to generate novel output patterns through various forms of perturbation applied to it, a critic neural network likewise capable of training in situ within this system, that learns to associate such novel patterns with their utility or value while triggering reinforcement learning of the more useful or valuable of these patterns within the former net. The device is capable of bootstrapping itself to progressively higher levels of adaptive or creative competence, starting from no learning whatsoever, through cumulative cycles of experimentation and learning. Optional feedback mechanisms between the latter and former self-learning artificial neural networks are used to accelerate the convergence of this system toward useful concepts or plans of action.

Abstract: A neural network development and data analysis tool provides significantly simplified network development through use of a scripted programming language, such as Extended Markup Language, or a project “wizard.” The system also provides various tools for analysis and use of a trained artificial neural network, including three-dimensional views, skeletonization, and a variety of output module options. The system also provides for the possibility of autonomous evaluation of a network being trained by the system and the determination of optimal network characteristics for a given set of provided data.