Abstract: The present disclosure provides automated systems and methods that facilitate consummation of Volume Weighted Average Price (“VWAP”) transactions and that support crossing of offsetting orders, cancellation of orders and enhanced liquidity for system users. Exemplary embodiments of the present disclosure provide a pre-open crossing network, an approximation engine and an intra-day crossing network. A processing engine is provided that is in communication with an algorithmic module and a database which advantageously includes a liquidity database. The processing engine is programmed to automatically access the liquidity database to determine an acceptable quantity of shares for trading in response to, and based upon, a requested user trade received by the processing engine. The algorithmic module includes programming for establishing a trading regimen for effecting trades that approach or achieve a VWAP price for best efforts VWAP trades.

Abstract: Pattern categorization is provided by a self-organizing analog field/layer which learns many-to-many, analog spatiotemporal mappings. The field/layer employs a set of input nodes, each input node having two long term memory weights, and a set of output nodes. Each input node is for categorizing patterns with respect to a plurality of categories. The long term memory weights of an input node encode the patterns categorized by the input node. Each input node generates signals as a function of respective long term memory weights and input signals to the input node. Each input node is coupled to a different output node. Each output node receives signals generated by the respective input node and selects a category of the respective input node. The output nodes provide a mapping between plural parts of the input pattern and plural categories of the input nodes. Category selections of the output nodes are modified such that sum of the output signals from the output nodes is within a predefined range.

Abstract: Pattern categorization is provided by a self-organizing analog field/layer which learns many-to-many, analog spatiotemporal mappings. The field/layer employs a set of input nodes, each input node having two long term memory weights, and a set of output nodes. Each input node is for categorizing patterns with respect to a plurality of categories. The long term memory weights of an input node encode the patterns categorized by the input node. Each input node generates signals as a function of respective long term memory weights and input signals to the input node. Each input node is coupled to a different output node. Each output node receives signals generated by the respective input node and selects a category of the respective input node. The output nodes provide a mapping between plural parts of the input pattern and plural categories of the input nodes. Category selections of the output nodes are modified such that sum of the output signals from the output nodes is within a predefined range.