Neural Networks For Knowledge Engineering

Description:-An artificial neural network is a biologically inspired computational model that consists of processing elements and connections between them. The structure of an artificial neuron is defined by inputs, having weights bound to them; an inputfunction, which calculates the aggregated net input signal to a neuron coming from all its inputs; anactivation (signal) function, which calculates the activation level of a neuron as a function of itsaggregated input signal and (possibly) of its previous state. An output signal equal to the activation valueis emitted through the output (the axon) of the neuron.

Neural networks are also called connectionist models owing to the main role of the connections. Theweights bound to them are a result of the training process and represent the "long-term memory" of themodel.

 The main characteristics of a neural network are:

  • Learning—a network can start with "no knowledge" and can be trained using a given set of dataexamples, that is, input-output pairs (a supervised training), or only input data (unsupervised training);through learning, the connection weights change in such a way that the network learns to produce desiredoutputs for known inputs; learning may require repetition.
  • Generalization—if a new input vector that differs from the known examples is supplied to thenetwork, it produces the best output according to the examples used.
  • Massive potential parallelism—during the processing of data, many neurons "fire" simultaneously.
  • Robustness—if some neurons "go wrong," the whole system may still perform well.
  • Partial match is what is required in many cases as the already known data do not coincide exactlywith the new facts

These main characteristics of neural networks make them useful for knowledge engineering. Neuralnetworks can be used for building expertsystems. They can be trained by a set of examples (data) and in that way they represent the "hidden" knowledge of an expert system.

For example, if we have good clinical records about patients suffering from cancer, we can use the data to train a neural network. The same network can also accommodate expertise provided by experts where the expertise is represented in an explicit form. After that, the network can recognize the health status of a new patient and make recommendations. Neural networks can be used effectively for building user interface to an expert system. There are connectionist models for natural language processing, speech recognition, pattern recognition, image processing, and so forth. The knowledge-engineering applications of neural networks inspire new connectionist models and new hypotheses about cognitive processes in the brain. Neural networks have been applied to almost every application area, where a data set is available and a good solution is sought. Neural networks can cope with noisy data, missing data, imprecise or corrupted data, and still produce a good solution.