Posted   by 

Neuro Fuzzy Soft Computing Solution Manual

Prerequisites:Genetic algorithms, Artificial Neural Networks, Fuzzy Logic

Unlike static PDF Neuro-Fuzzy and Soft Computing solution manuals or printed answer keys, our experts show you how to solve each problem step-by-step. No need to wait for office hours or assignments to be graded to find out where you took a wrong turn. You can check your reasoning as you tackle a problem using our interactive solutions viewer. Neuro-Fuzzy and Soft Computing: Fuzzy Sets 32 If f(.) is a first order polynomial, then the resulting fuzzy inference is called a first order Sugeno fuzzy model If f(.) is a constant then it is a zero order Sugeno fuzzy model (special case of Mamdani model) Case of two rules with a first order Sugeno fuzzy model.Each rule has a crisp output. Neuro-Fuzzy and Soft Computing is a Ten! Wierman, Center for Research in Fuzzy Mathematics and Computer Science, Creighton Univeristy ' Neuro-Fuzzy and Soft Computing, as a mature and extensive coverage of neuro-fuzzy soft computing, demonstrates a paradigm shift in managing complexity, uncertainty and subjectivity.

Neuro Fuzzy Soft Computing Solution Manual

Hybrid systems: A Hybrid system is an intelligent system which is framed by combining atleast two intelligent technologies like Fuzzy Logic, Neural networks, Genetic algorithm, reinforcement Learning, etc. The combination of different techniques in one computational model make these systems possess an extended range of capabilities. These systems are capable of reasoning and learning in an uncertain and imprecise environment. These systems can provide human-like expertise like domain knowledge, adaptation in noisy environment etc.

Types of Hybrid Systems:

  • Neuro Fuzzy Hybrid systems
  • Neuro Genetic Hybrid systems
  • Fuzzy Genetic Hybrid systems

(A) Neuro Fuzzy Hybrid systems:

Neuro fuzzy system is based on fuzzy system which is trained on the basis of working of neural network theory. The learning process operates only on the local information and causes only local changes in the underlying fuzzy system. A neuro-fuzzy system can be seen as a 3-layer feedforward neural network. The first layer represents input variables, the middle (hidden) layer represents fuzzy rules and the third layer represents output variables. Fuzzy sets are encoded as connection weights within the layers of the network, which provides functionality in processing and training the model.


Working flow:

  • In input layer, each neuron transmits external crisp signals directly to the next layer.
  • Each fuzzification neuron receives a crisp input and determines the degree to which the input belongs to input fuzzy set.
  • Fuzzy rule layer receives neurons that represent fuzzy sets.
  • An output neuron, combines all inputs using fuzzy operation UNION.
  • Each defuzzification neuron represents single output of neuro-fuzzy system.

Advantages:

  • It can handle numeric, linguistic, logic, etc kind of information.
  • It can manage imprecise, partial, vague or imperfect information.
  • It can resolve conflicts by collaboration and aggregation.
  • It has self-learning, self-organizing and self-tuning capabilities.
  • It can mimic human decision-making process.

Disadvantages:

  • Hard to develop a model from a fuzzy system
  • Problems of finding suitable membership values for fuzzy systems
  • Neural networks cannot be used if training data is not available.

Applications:

Neuro Fuzzy Soft Computing Solution Manual
  • Student Modelling
  • Medical systems
  • Traffic control systems
  • Forecasting and predictions
Neuro fuzzy soft computing solution manual 2017

(B) Neuro Genetic Hybrid systems:

A Neuro Genetic hybrid system is a system that combines Neural networks: which are capable to learn various tasks from examples, classify objects and establish relation between them and Genetic algorithm: which serves important search and optimization techniques. Genetic algorithms can be used to improve the performance of Neural Networks and they can be used to decide the connection weights of the inputs. These algorithms can also be used for topology selection and training network.



Working Flow:

  • GA repeatedly modifies a population of individual solutions. GA uses three main types of rules at each step to create the next generation from the current population:
    1. Selection to select the individuals, called parents, that contribute to the population at the next generation
    2. Crossover to combine two parents to form children for the next generation
    3. Mutation to apply random changes to individual parents in order to form children
  • GA then sends the new child generation to ANN model as new input parameter.
  • Finally, calculating of the fitness by developed ANN model is performed.

Advantages:

  • GA is used for topology optimization i.e to select number of hidden layers, number of hidden nodes and interconnection pattern for ANN.
  • In GAs, the learning of ANN is formulated as a weight optimization problem, usually using the inverse mean squared error as a fitness measure.
  • Control parameters such as learning rate, momentum rate, tolerance level, etc are also optimized using GA.
  • It can mimic human decision-making process.

Disadvantages:

  • Highly complex system.
  • Accuracy of the system is dependent on the initial population.
  • Maintaintainance costs are very high.

Applications:

  • Face recognition
  • DNA matching
  • Animal and human research
  • Behavioral system

(C) Fuzzy Genetic Hybrid systems:

A Fuzzy Genetic Hybrid System is developed to use fuzzy logic based techniques for improving and modelling Genetic algorithms and vice-versa. Genetic algorithm has proved to be a robust and efficient tool to perform tasks like generation of fuzzy rule base, generation of membership function etc.
Three approaches that can be used to develop such system are:

  • Michigan Approach
  • Pittsburgh Approach
  • IRL Approach

Working Flow:

  • Start with an initial population of solutions that represent first generation.
  • Feed each chromosome from the population into the Fuzzy logic controller and compute performance index.
  • Create new generation using evolution operators till some condition is met.

Advantages:

  • GAs are used to develop the best set of rules to be used by a fuzzy inference engine
  • GAs are used to optimize the choice of membership functions.
  • A Fuzzy GA is a directed random search over all discrete fuzzy subsets.
  • It can mimic human decision-making process.

Disadvantages:

Neuro Fuzzy Soft Computing Solution Manual Solution

  • Interpretation of results is difficult.
  • Difficult to build membership values and rules.
  • Takes lots of time to converge.

Applications:

  • Mechanical Engineering
  • Electrical Engine
  • Artificial Intelligence
  • Economics

Sources:
(1)https://en.wikipedia.org/wiki/Hybrid_intelligent_system
(2)Principles of Soft Computing

Attention reader! Don’t stop learning now. Get hold of all the important CS Theory concepts for SDE interviews with the CS Theory Course at a student-friendly price and become industry ready.

Recommended Posts:

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Neuro Fuzzy Soft Computing Solution Manual Pdf

Please Improve this article if you find anything incorrect by clicking on the 'Improve Article' button below.

Neuro Fuzzy Manual