- Artificial Neural Network Tutorial
- Artificial Neural Network - Home
- Basic Concepts
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- Learning Vector Quantization
- Adaptive Resonance Theory
- Kohonen Self-Organizing Feature Maps
- Associate Memory Network
- Hopfield Networks
- Boltzmann Machine
- Brain-State-in-a-Box Network
- Optimization Using Hopfield Network
- Other Optimization Techniques
- Genetic Algorithm
- Applications of Neural Networks
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- Discussion
Applications of Neural Networks
Before studying the fields where ANN has been used extensively, we need to understand why ANN would be the preferred choice of application.
Why Artificial Neural Networks?
We need to understand the answer to the above question with an example of a human being. As a child, we used to learn the things with the help of our elders, which includes our parents or teachers. Then later by self-learning or practice we keep learning throughout our life. Scientists and researchers are also making the machine intelligent, just like a human being, and ANN plays a very important role in the same due to the following reasons −
With the help of neural networks, we can find the solution of such problems for which algorithmic method is expensive or does not exist.
Neural networks can learn by example, hence we do not need to program it at much extent.
Neural networks have the accuracy and significantly fast speed than conventional speed.
Areas of Application
Followings are some of the areas, where ANN is being used. It suggests that ANN has an interdisciplinary approach in its development and applications.
Speech Recognition
Speech occupies a prominent role in human-human interaction. Therefore, it is natural for people to expect speech interfaces with computers. In the present era, for communication with machines, humans still need sophisticated languages which are difficult to learn and use. To ease this communication barrier, a simple solution could be, communication in a spoken language that is possible for the machine to understand.
Great progress has been made in this field, however, still such kinds of systems are facing the problem of limited vocabulary or grammar along with the issue of retraining of the system for different speakers in different conditions. ANN is playing a major role in this area. Following ANNs have been used for speech recognition −
Multilayer networks
Multilayer networks with recurrent connections
Kohonen self-organizing feature map
The most useful network for this is Kohonen Self-Organizing feature map, which has its input as short segments of the speech waveform. It will map the same kind of phonemes as the output array, called feature extraction technique. After extracting the features, with the help of some acoustic models as back-end processing, it will recognize the utterance.
Character Recognition
It is an interesting problem which falls under the general area of Pattern Recognition. Many neural networks have been developed for automatic recognition of handwritten characters, either letters or digits. Following are some ANNs which have been used for character recognition −
- Multilayer neural networks such as Backpropagation neural networks.
- Neocognitron
Though back-propagation neural networks have several hidden layers, the pattern of connection from one layer to the next is localized. Similarly, neocognitron also has several hidden layers and its training is done layer by layer for such kind of applications.
Signature Verification Application
Signatures are one of the most useful ways to authorize and authenticate a person in legal transactions. Signature verification technique is a non-vision based technique.
For this application, the first approach is to extract the feature or rather the geometrical feature set representing the signature. With these feature sets, we have to train the neural networks using an efficient neural network algorithm. This trained neural network will classify the signature as being genuine or forged under the verification stage.
Human Face Recognition
It is one of the biometric methods to identify the given face. It is a typical task because of the characterization of “non-face” images. However, if a neural network is well trained, then it can be divided into two classes namely images having faces and images that do not have faces.
First, all the input images must be preprocessed. Then, the dimensionality of that image must be reduced. And, at last it must be classified using neural network training algorithm. Following neural networks are used for training purposes with preprocessed image −
Fully-connected multilayer feed-forward neural network trained with the help of back-propagation algorithm.
For dimensionality reduction, Principal Component Analysis (PCA) is used.