Abstract: There is disclosed a pattern identifying neural network comprising at least an input and an output layer, the output layer having a plurality of principal nodes, each principal node trained to recognize a different class of patterns, and at least one fuzzy node trained to recognize all classes of patterns recognized by the principal nodes but with outputs set out at levels lower than the corresponding outputs of the principal nodes.

Abstract: A recognition apparatus and method using a neural network is provided. A neuron-like element stores a value of its inner condition. The neuron-like element also updates a values of its internal status on the basis of an output from the neuron-like element itself, outputs from other neuron-like elements and an external input, and an output value generator a value of its internal status into an external output. Accordingly, the neuron-like element itself can retain the history of input data. This enables the time series data, such as speech, to be processed without providing any special devices in the neural network.

Abstract: Disclosed is a system and method for a profiling system wherein the collection of profile data may be enabled and disabled during the execution of an instrumented computer program. The invention utilizes a dedicated bit in a condition register which is checked each time a block of instrumentation code is to be executed. If the bit is enabled, the instrumentation code is executed and profile data is collected. If the bit is not enabled, then the instrumentation block is skipped and not executed and no profile information is collected. The enabling and disabling of the bit in the condition register may be implemented in hardware, in software, or in a combination of the two.