Abstract: We disclose an apparatus for improving passing skills in soccer, consisting of one or more triangles that can be placed on the ground to demarcate regions and boundaries on a playing field within which player movement is constrained or otherwise guided. The triangle edges are made from a flexible or extensible material and are attached at the triangle vertices by some type of joining device. The triangles can be placed on any surface suitable for soccer and held in place with stakes or other means. Multiple triangles can be placed on the ground to create a variety of demarcated regions and boundaries. This apparatus can be used for basic practice or to play various skill-enhancing games in which the ball is passed into and out of, or between, a triangle or a set of triangles.

Abstract: We disclose an apparatus for improving passing skills in soccer, consisting of one or more triangles that can be placed on the ground to demarcate regions and boundaries on a playing field within which player movement is constrained or otherwise guided. The triangle edges are made from a flexible or extensible material and are attached at the triangle vertices by some type of joining device. The triangles can be placed on any surface suitable for soccer and held in place with stakes or other means. Multiple triangles can be placed on the ground to create a variety of demarcated regions and boundaries. This apparatus can be used for basic practice or to play various skill-enhancing games in which the ball is passed into and out of, or between, a triangle or a set of triangles.

Abstract: A method for evolving appropriate connections among units in a neural network includes a) calculating weight changes at each existing connection and incipient connections between units for each training example; and b) determining a K ratio using the weight changes, wherein said K ratio comprises the weight change of existing connections, and wherein if the K ratio the weight change of incipient connections exceeds a threshold, further including b1) increasing a weight of the existing connection; b2) creating new connections at the incipient connections. The method further includes c) pruning weak connections between the units.

Abstract: A system and method for evolving appropriate connections in feedforward topological networks. FIG. 1 illustrates an exemplary flow diagram of a method for evolving appropriate connections in a neural network device according to an aspect of the present invention. Initially, weight changes induced by each particular training sample or pattern are calculated using, for example, a conventional network training rule such as Hebb or backpropagation (step 101). Next, a ratio of the weight changes for existing connections to incipient connections (“K” ratio) is calculated (step 103). If this K ratio exceeds a specified threshold, weight changes are implemented (step 105), in which existing weights are increased by (1−E)×(total weight change of existing connections), where E is a network-wide parameter. The remaining amount (E)×(total weight change of existing connections) is added to form neighboring connections (step 107).