Advanced Mortgage Risk Evaluation and Noise Reduction in Telecommunication Systems
This document explores innovative applications in mortgage risk evaluation using sophisticated underwriting processes to assess delinquency risk, origination, and insurance. Additionally, it discusses the implementation of adaptive filters to reduce transmission errors in telecommunication systems, thereby enhancing clarity. The integration of advanced technologies such as bomb detection systems utilizing gamma-ray emissions at JFK Airport illustrates the importance of safety in public spaces. Also included are theoretical frameworks like Hopfield Networks for associative memory and training rules for effective pattern recognition.
Advanced Mortgage Risk Evaluation and Noise Reduction in Telecommunication Systems
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Presentation Transcript
NN applications • Mortage risk evaluator appraises underwriting process Deliquency risk mortage origination mortage insurance Assessment underwriting underwiriting • Clean up noise on telephone lines and reduce transmission errors on modems (adaptive filters). • SNOOPE – bomb detector system at JFK. Detect explosives based on γ – ray emissions. • Airline Marketing Tactician (AMT) advices seat yield management.
Hopfield Nets(Associative Memories) Wij = Wji -1 -1 1 3 -1 1 -2 3 2 1 -1 Si = sgn(Σ Wij Sj ) j Si =Activation of node i Sgn(X) = 1, x>=0 -1, x<0
Parellel relaxation • Stable states
Memorize patterns: ξμ , μ= 1,2,…,p • Where ξμ = (ξ1μ , …., ξNμ ) Σμ • Attractors (net nodes labeled 1,…,N) Learning Rule: Hebb rule: Wij= (1/N) * Σ ξiμ ξjμ p μ = 1 • Asynchronus unitupdating • To recall perfectly p attractors with N units • P= N/ (4 log N)
To train hopfield 1 2 To stroe pattern Wij =1 if both states have same activation = -1 otherwise 4 3 5 7 6
To train hopfield (contd..) = W1 1 2 3 4 5 6 7 • 0 -1 1 -1 0 0 0 • 0 0 1 0 0 0 • 0 -1 1 1 0 • 0 0 -1 1 • 0 1 0 • 0 -1 • 0 Total net weights: W = W1 +W2 +… One matrix per pattern
