I welcome you all to this presentation On:

Neural Network Applications Systems Engineering Dept. KFUPM Imran Nadeem & Naveed R. Butt &

Part II: LMS & RBFPart I: Introduction to Neural NetworksPart III: Control Applications

Part I: Introduction to Neural Networks Part I: Introduction to NN’s  There is no restriction on the unknown function to be linear. Thus, neural networks provide a logical extension to create nonlinear adaptive control schemes.  Universal Approximation Theorem: neural networks can reproduce any nonlinear function for a limited input set.  Neural networks are parameterized nonlinear functions whose parameters can be adjusted to achieve different shaped nonlinearities.  In essence, we try to adjust the neural network to serve as an approximator for an unknown function that we know only through its inputs and outputs

Human Neuron Part I: Introduction to Neural Networks

Artificial Neuron Part I: Introduction to Neural Networks

Adaptation in NN’s Part I: Introduction to Neural Networks

Single Layer Feedforward NN’s Part I: Introduction to Neural Networks

Multi-Layer Feedforward NN’s

Recurrent (feedback) NN’s Part I: Introduction to Neural Networks A recurrent neural network distinguishes itself from the feed-forward network in that it has at least one feedback loop. For example, a recurrent network may consist of a single layer of neurons with each neuron feeding its output signal back to the input of all input neurons.

Recurrent (feedback) NN’s Part I: Introduction to Neural Networks The presence of feedback loops has a profound impact on the learning capability of the network and on its performance.

Applications of NN’s Part I: Introduction to Neural Networks Neural networks are applicable in virtually every situation in which a relationship between the predictor variables (independents, inputs) and predicted variables (dependents, outputs) exists, even when that relationship is very complex and not easy to articulate in the usual terms of "correlations" or "differences between groups”

Applications of NN’s Part I: Introduction to Neural Networks  Detection of medical phenomena  Stock market prediction  Credit assignment  Condition Monitoring  Signature analysis  Process control  Nonlinear Identification & Adaptive Control

End of Part I

Part II: LMS & RBF Part II: LMS & RBF LMS: The Adaptation Algorithm RBF: Radial Bases Function NN

Part II: LMS & RBF LMS: The Adaptation Algo. Estimation Error Actual Response Estimated Response Cost Function Mean Square Error Weight Updates Adaptation Step Size

Part II: LMS & RBF RBF-NN’s Radial functions are a special class of functions. Their characteristic feature is that their response decreases (or increases) monotonically with distance from a central point and they are radially symmetric.

Part II: LMS & RBF RBF-NN’s Gaussian RBF

Part II: LMS & RBF RBF-NN’s Neural Networks based on radial bases functions are known as RBF Neural Networks and are among the most commonly used Neural Networks

Part II: LMS & RBF RBF-NN’s  Two-layer feed-forward networks.  Hidden nodes: radial basis functions.  Output nodes : linear summation.  Very fast learning  Good for interpolation, estimation & Classification

Part III: Control Applications Part III: Control Applications Nonlinear System Identification Adaptive Tracking of Nonlinear Plants

Nonlinear System Identification Part III: Control Applications

Nonlinear System Identification Part III: Control Applications Continuously Stirred Tank Reactor

Nonlinear System Identification Part III: Control Applications Simulation Results Using SIMULINK

Adaptive Nonlinear Tracking Part III: Control Applications

Adaptive Nonlinear Tracking Part III: Control Applications Hammerstein Model

Adaptive Nonlinear Tracking Part III: Control Applications Simulation Results Using SIMULINK

Adaptive Nonlinear Tracking Part III: Control Applications Simulation Results Using SIMULINK

Thank you