My Final Year Project tittle

Posture Recognition using RBF and MLP Neural Network

*now maybe change to defective tin a.k.a tin rosak recognition =.= '''

Some brieft introduction on field of my fyp [boring literature review]

Artificial neural networks can be most adequately characterized as “computational model” with the properties such as the ability to adapt and learn, to generalize, to cluster or organize data where all the operations are working based on parallel processing. For the above properties, there are some other existing non neural models can be attributed. Artificial neural networks are proven to be a better approach for certain applications.

The utility of artificial neural network models lie in the fact that they can be used to infer a function from observations. It comes in hand in applications where the complexity of the data or task makes the design of such a function by hand impractical. Artificial neural network are applied in broad categories from functional approximation (time series prediction, fitness approximation and modeling), classification (pattern and sequence recognition, sequential decision making), data processing (filtering, clustering, compression) and robotics (directing manipulators) ^[1]

RBF network (Radial basis function) and MLP network (multi-layer perceptron) are commonly involved in artificial neural network classification application. A RBF network is an artificial neural network that uses radial basis functions as activation functions. Generally, it consists of two layers of processing. The input is mapped onto each RBF in the hidden layer. The RBF is usually a Gaussian. A MLP is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate output. It uses there or more layers of neurons with nonlinear activation function.

MLP


RBF

I think most of the people wont know what it is and wont interested to know it also ..... maybe a real life example will be easier

When we taking photo with camera, there is a function as smile detection or face detection. It will capture the motion of the face and snap when there is a smile or focus on the face (theoretically >.<). Basically, the input is the image capture by the camera and the input have been trained with ANN(RBF,MLP, SVM etc) to perform the function. The neuron is train with the image such as face or non face before its perform the function . The theory of how my FYP working is similar to this one....

* i doubt do people can understand this o not ???