Artificial Intelligence, Expert Systems, and Neural Networks
... neural network that takes inputs and produces outputs. ...
... neural network that takes inputs and produces outputs. ...
Training
... needed to uniquely describe its future behavior, except for the purely external effects arising from the applied input (excitation). Let the q-by-1 vector x(n) denote the state of a nonlinear discrete-time system. Let the m-by-1 vector u(n) denote the input applied to the system, and the pby-1 vecto ...
... needed to uniquely describe its future behavior, except for the purely external effects arising from the applied input (excitation). Let the q-by-1 vector x(n) denote the state of a nonlinear discrete-time system. Let the m-by-1 vector u(n) denote the input applied to the system, and the pby-1 vecto ...
Topic 4
... ANN-based systems not likely to replace conventional computing systems, but they are an established alternative to the symbolic logic approach to ...
... ANN-based systems not likely to replace conventional computing systems, but they are an established alternative to the symbolic logic approach to ...
Midterm Guide
... 3. Genetic algorithms: Design of a genetic algorithm Genetic encoding/decoding of a problem Genetic operators Objective function 4. Neural networks: Neural networks versus statistical methods Supervised versus Unsupervised learning Linearly separable problems Detailed design and impl ...
... 3. Genetic algorithms: Design of a genetic algorithm Genetic encoding/decoding of a problem Genetic operators Objective function 4. Neural networks: Neural networks versus statistical methods Supervised versus Unsupervised learning Linearly separable problems Detailed design and impl ...
Artificial Intelligence CSC 361
... 1960s: Widrow and Hoff explored Perceptron networks (which they called “Adalines”) and the delta rule. ...
... 1960s: Widrow and Hoff explored Perceptron networks (which they called “Adalines”) and the delta rule. ...
2806nn1
... desired response. Note, both positive and negative examples are possible. A set of input-output pairs, with each pair consisting of an input signal and the corresponding desired response, is referred to as a set of training data or training sample. ...
... desired response. Note, both positive and negative examples are possible. A set of input-output pairs, with each pair consisting of an input signal and the corresponding desired response, is referred to as a set of training data or training sample. ...
Project #2
... networks. Each such text file might represent a neural network that has already been trained based on specific data, or it might represent an untrained network with initial weights that have been either manually configured or randomly generated. Your code should not randomly generate weights, so thi ...
... networks. Each such text file might represent a neural network that has already been trained based on specific data, or it might represent an untrained network with initial weights that have been either manually configured or randomly generated. Your code should not randomly generate weights, so thi ...
Organization of Behavior
... act on central pattern generators changes in activity in brainstem "command" circuits directed by sensory input + or klinotaxis (single receptor compares stimulus over time) tropotaxis (paired receptors--simultaneous comparison) telotaxis (toward a goal--e.g. swim toward shore) not well studied in v ...
... act on central pattern generators changes in activity in brainstem "command" circuits directed by sensory input + or klinotaxis (single receptor compares stimulus over time) tropotaxis (paired receptors--simultaneous comparison) telotaxis (toward a goal--e.g. swim toward shore) not well studied in v ...
APPLICATION OF AN EXPERT SYSTEM FOR ASSESSMENT OF …
... called synapses. The junctions pass a large signal across, whilst others are very poor. The cell body receives all inputs, and fires if the total input exceeds the threshold. Our model of the neuron must capture these important features: ...
... called synapses. The junctions pass a large signal across, whilst others are very poor. The cell body receives all inputs, and fires if the total input exceeds the threshold. Our model of the neuron must capture these important features: ...
NeuralNets
... This hidden unit detects a mildly left sloping road and advices to steer left. How would another hidden unit look like? ...
... This hidden unit detects a mildly left sloping road and advices to steer left. How would another hidden unit look like? ...
No Slide Title
... backward from output nodes to input nodes and in fact can have arbitrary connections between any nodes. • While learning, the recurrent network feeds its inputs through the network including feeding data back from outputs to inputs and repeat this process until the values of the outputs do not chang ...
... backward from output nodes to input nodes and in fact can have arbitrary connections between any nodes. • While learning, the recurrent network feeds its inputs through the network including feeding data back from outputs to inputs and repeat this process until the values of the outputs do not chang ...
Project #2
... networks. Each such text file might represent a neural network that has already been trained based on specific data, or it might represent an untrained network with initial weights that have been either manually configured or randomly generated. Your code should not randomly generate weights, so thi ...
... networks. Each such text file might represent a neural network that has already been trained based on specific data, or it might represent an untrained network with initial weights that have been either manually configured or randomly generated. Your code should not randomly generate weights, so thi ...
An Artificial Neural Network for Data Mining
... Abstract: Data mining is a logical process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process or knowledge mining from data. The goal of this technique is to find patterns that were previously unknown and once these patterns are found th ...
... Abstract: Data mining is a logical process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process or knowledge mining from data. The goal of this technique is to find patterns that were previously unknown and once these patterns are found th ...
Unsupervised Learning
... • So far the ordering of the output units themselves was not necessarily informative • The location of the winning unit can give us information regarding similarities in the data • We are looking for an input output mapping that conserves the topologic properties of the inputs feature mapping • Gi ...
... • So far the ordering of the output units themselves was not necessarily informative • The location of the winning unit can give us information regarding similarities in the data • We are looking for an input output mapping that conserves the topologic properties of the inputs feature mapping • Gi ...
14/15 April 2008
... How Does It Work? Nodes are modelled by conventional binary MP neurons. Each neuron serves both as an input and output unit. (There are no hidden units.) States are given by the pattern of activity of the neurons (e.g. 101 for a network with three neurons). The number of neuron sets the maximum len ...
... How Does It Work? Nodes are modelled by conventional binary MP neurons. Each neuron serves both as an input and output unit. (There are no hidden units.) States are given by the pattern of activity of the neurons (e.g. 101 for a network with three neurons). The number of neuron sets the maximum len ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.