Time delay neural network
Encyclopedia
Time delay neural network (TDNN) is an alternative neural network
Neural network
The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes...

 architecture
Network architecture
Network architecture is the design of a communications network. It is a framework for the specification of a network's physical components and their functional organization and configuration, its operational principles and procedures, as well as data formats used in its operation.In...

 whose primary purpose is to work on continuous data. The advantage of this architecture is to adapt the network online and hence helpful in many real time
Real-time computing
In computer science, real-time computing , or reactive computing, is the study of hardware and software systems that are subject to a "real-time constraint"— e.g. operational deadlines from event to system response. Real-time programs must guarantee response within strict time constraints...

 applications, like time series prediction, online spell check
Spell checker
In computing, a spell checker is an application program that flags words in a document that may not be spelled correctly. Spell checkers may be stand-alone capable of operating on a block of text, or as part of a larger application, such as a word processor, email client, electronic dictionary,...

, continuous speech recognition
Speech recognition
Speech recognition converts spoken words to text. The term "voice recognition" is sometimes used to refer to recognition systems that must be trained to a particular speaker—as is the case for most desktop recognition software...

,etc.

The architecture has a continuous input that is delayed and sent as an input to the neural network. As an example, consider training a feed forward neural network being trained for a time series prediction. The desired output of the network is the present state of the time series and inputs to the neural network are the delayed time series (past values). Hence, the output of the neural network is the predicted next value in the time series which is computed as the function of the past values of the time series.
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