An application: A Client Financial Risk Tolerance Model

Slides:



Advertisements
Similar presentations

Advertisements

 Negnevitsky, Pearson Education, Lecture 5 Fuzzy expert systems: Fuzzy inference n Mamdani fuzzy inference n Sugeno fuzzy inference n Case study.
AI – CS364 Fuzzy Logic Fuzzy Logic 3 03 rd October 2006 Dr Bogdan L. Vrusias
Fuzzy Logic and its Application to Web Caching
Contoh Soal Fuzzy.
Fuzzy Inference and Defuzzification
Fuzzy logic Fuzzy Expert Systems Yeni Herdiyeni Departemen Ilmu Komputer.
Fuzzy Logic E. Fuzzy Inference Engine. “antecedent” “consequent”
Fuzzy Expert System.
Fuzzy Logic E. Fuzzy Inference Engine. “antecedent” “consequent”
WELCOME TO THE WORLD OF FUZZY SYSTEMS. DEFINITION Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept.
Ming-Feng Yeh General Fuzzy Systems A fuzzy system is a static nonlinear mapping between its inputs and outputs (i.e., it is not a dynamic system).
The Equivalence between fuzzy logic controllers and PD controllers for single input systems Professor: Chi-Jo Wang Student: Nguyen Thi Hoai Nam Student.
Fuzzy Rule-based Models *Neuro-fuzzy and Soft Computing - J.Jang, C. Sun, and, E. Mizutani, Prentice Hall 1997.
Rule-Based Fuzzy Model. In rule-based fuzzy systems, the relationships between variables are represented by means of fuzzy if–then rules of the following.
Fuzzy Rules 1965 paper: “Fuzzy Sets” (Lotfi Zadeh) Apply natural language terms to a formal system of mathematical logic
Lecture 5 Fuzzy expert systems: Fuzzy inference Mamdani fuzzy inference Mamdani fuzzy inference Sugeno fuzzy inference Sugeno fuzzy inference Case study.
 Negnevitsky, Pearson Education, Lecture 5 Fuzzy expert systems: Fuzzy inference n Mamdani fuzzy inference n Sugeno fuzzy inference n Case study.
Section 1.3 Functions. What you should learn How to determine whether relations between two variables are functions How to use function notation and evaluate.
Fuzzy Inference (Expert) System
Ming-Feng Yeh Fuzzy Control The primary goal of control engineering is to distill and apply knowledge about how to control a process so that the.
Neural-Network-Based Fuzzy Logical Control and Decision System 主講人 虞台文.
Fuzzy Systems Michael J. Watts
Fuzzy Inference Systems. Fuzzy inference (reasoning) is the actual process of mapping from a given input to an output using fuzzy logic. The process involves.
PART 9 Fuzzy Systems 1. Fuzzy controllers 2. Fuzzy systems and NNs 3. Fuzzy neural networks 4. Fuzzy Automata 5. Fuzzy dynamic systems FUZZY SETS AND FUZZY.
“Principles of Soft Computing, 2 nd Edition” by S.N. Sivanandam & SN Deepa Copyright  2011 Wiley India Pvt. Ltd. All rights reserved. CHAPTER 12 FUZZY.
We are learning to write expressions using variables. (8-1)
Homework 5 Min Max “Temperature is low” AND “Temperature is middle”
LESSON 7.4 Function Notation To learn function notation To evaluate functions by substitution, by using the graphs drawn by hand, and on the graphing calculator.
Chapter 10 Fuzzy Control and Fuzzy Expert Systems
© South-Western Educational Publishing Chapter 11 Investing for Your Future  Investing Fundamentals  Exploring Investment Options.
McGraw-Hill/Irwin © The McGraw-Hill Companies, All Rights Reserved CHAPTER 9 Enabling the Organization—Decision Making.
1 Lecture 4 The Fuzzy Controller design. 2 By a fuzzy logic controller (FLC) we mean a control law that is described by a knowledge-based system consisting.
Dinner for Two. Fuzzify Inputs Apply Fuzzy Operator.
© Negnevitsky, Pearson Education, Lecture 5 Fuzzy expert systems: Fuzzy inference Mamdani fuzzy inference Mamdani fuzzy inference Sugeno fuzzy inference.
Algebra 2 Foundations, pg 64  Students will be able to graph relations and identify functions. Focus Question What are relations and when is a relation.
Function Let be a mapping. If both A and B are sets of real numbers, we say that f is a function or, more precisely, a real function in one variable. When.
Introduction to Fuzzy Logic and Fuzzy Systems
Fuzzy Inference System
Fuzzy Logic Control What is Fuzzy Logic ? Logic and Fuzzy Logic
Artificial Intelligence CIS 342
Fuzzy Systems Michael J. Watts
FUZZY NEURAL NETWORKS TECHNIQUES AND THEIR APPLICATIONS
An Intelligent Approach for Nuclear Security Measures on Nuclear Materials: Demands and Needs Authors: A.Z.M. Salahuddin, Altab Hossain, R. A. Khan, M.S.
Chapter Functions.
Fuzzy expert systems Fuzzy inference Mamdani fuzzy inference
Building a Fuzzy Expert System
Artificial Intelligence
Homework 8 Min Max “Temperature is low” AND “Temperature is middle”
Introduction to Fuzzy Logic
Relations and Functions Pages
Fuzzy logic Introduction 3 Fuzzy Inference Aleksandar Rakić
Model Functions Input x 6 = Output Input x 3 = Output
Fuzzy System Structure
منطق فازی.
Dr. Unnikrishnan P.C. Professor, EEE
Dr. Unnikrishnan P.C. Professor, EEE
Lecture 35 Fuzzy Logic Control (III)
Homework 9 Min Max “Temperature is low” AND “Temperature is middle”
C Graphing Functions.
Lecture 5 Fuzzy expert systems: Fuzzy inference
Function Rules and Tables.
FUNCTION NOTATION AND EVALUATING FUNCTIONS
Dr. Unnikrishnan P.C. Professor, EEE
Objective- To use an equation to graph the
Lesson 1.7 Represent Functions as Graphs
Fuzzy Inference Systems
UNDERSTANDING FUNCTIONS
Fuzzy Logic KH Wong Fuzzy Logic v.9a.
Lecture 35 Fuzzy Logic Control (III)
Presentation transcript:

An application: A Client Financial Risk Tolerance Model

A Client Financial Risk Tolerance Model Financial service institutions face a difficult task in evaluating clients risk tolerance. It is a major component for the design of an investment policy and understanding the implication of possible investment options in terms of safety and suitability. Here we present a simple model of client's risk tolerance ability which depends on his/hers annual income (AI) and total networth (TNW). The control objective of the client financial risk tolerance policy model is for any given pair of input variables (annual income, total networth) to and a corresponding output, a risk tolerance (RT) level.

A Client Financial Risk Tolerance Model Suppose the financial experts agree to describe the input variables annual income and total networth and the output variable risk tolerance by the sets,

A Client Financial Risk Tolerance Model The terms of the linguistic variables annual income, total networth, and risk tolerance described by triangular and part of trapezoidal numbers formally have the same membership functions presented analytically below,

A Client Financial Risk Tolerance Model Terms of the input annual income Terms of the input total networth Terms of the output risk tolerance

A Client Financial Risk Tolerance Model If : : : and : : : then Rules Assume that the financial experts selected the rules presented on the decision table

A Client Financial Risk Tolerance Model If : : : and : : : then Rules Rule 1: If client's annual income (CAI) is low (L) and client's total networth (CTN) is low (L), then client's risk tolerance (CRT) is low (L); Rule 2: If CAI is L and CTN is medium (M), then CRT is L; Rule 3: If CAI is L and CTN is high (H), then CRT is moderate (MO); Rule 4: If CAI is M and CTN is L, then CRT is L; Rule 5: If CAI is M and CTN is M, then CRT is MO; Rule 6: If CAI is M and CTN is H, then CRT is H; Rule 7: If CAI is H and CTN is L, then CRT is MO; Rule 8: If CAI is H and CTN is M, then CRT is H; Rule 9: If CAI is H and CTN is H, then CRT is H.

A Client Financial Risk Tolerance Model Fuzzification assuming readings: x0 = 40 in thousands (annual income) and y0 = 25 in ten of thousands (totalnetworth). They are matched against the appropriate terms. The fuzzy inputs are calculated. Note that x = 40 and y = 25 are substituted for v instead of 40,000 and 250,000 since x and y are measured in thousands and ten of thousands. The result is

A Client Financial Risk Tolerance Model Fuzzificaiton Fuzzy reading inputs for the clients financial risk tolerance model. Readings: x0 = 40 and y0 = 25

A Client Financial Risk Tolerance Model Fuzzy Operations Induced decision table for the clients financial risk tolerance model. There are four active rules, 1,2,4,5 Rule 1: If client's annual income (CAI) is low (L) and client's total networth (CTN) is low (L), then client's risk tolerance (CRT) is low (L); Rule 2: If CAI is L and CTN is medium (M), then CRT is L; Rule 4: If CAI is M and CTN is L, then CRT is L; Rule 5: If CAI is M and CTN is M, then CRT is MO;

A Client Financial Risk Tolerance Model Multiple conjuctive antecedents The strength of these rules or level of firing (the and part) is calculated as follows

A Client Financial Risk Tolerance Model Firing of rules for the client nancial risk tolerance model

A Client Financial Risk Tolerance Model Aggregation of Fuzzy Rules Geometrically this means that we have to superimpose trapezoids on top of one another in the same coordinate system. Aggregated output for the client financial risk tolerance model

A Client Financial Risk Tolerance Model Defuzzificaton The aggregated output for the client financial risk tolerance model can be defuzzified by the defuzzification methods.