C++ Neural Networks and Fuzzy Logic: Preface

Time Series Prediction with Recurrent and Nonrecurrent Networks

Sathyanarayan Rao and Sriram Sethuraman take a recurrent neural network and a feedforward network and

train then in parallel. A recurrent neural network has feedback connections from the output neurons back to

input neurons to model the storage of temporal information. A modified backpropagation algorithm is used

for training the recurrent network, called the real−time recurrent learning algorithm. They have the recurrent

neural network store past information, and the feedforward network do the learning of nonlinear dependencies

on the current samples. They use this scheme because the recurrent network takes more than one time period

to evaluate its output, whereas the feedforward network does not. This hybrid scheme overcomes the latency

problem for the recurrent network, providing immediate nonlinear evaluation from input to output.

Security Alarms

Deborah Frank and J. Bryan Pletta study the application of neural networks for alarm classification based on

their operation under varying weather conditions. Performance degradation of a security system when the

environment changes is a cause for losing confidence in the system itself. This problem is more acute with

portable security systems.

They investigated the problem using several networks, ranging from backpropagation to learning vector

quantization. Data was collected using many scenarios, with and without the coming of an intruder, which can

be a vehicle or a human.

They found a 98% probability of detection and 9% nuisance alarm rate over all weather conditions.

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