An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of

artificial neural network Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ...

that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks and

fuzzy logic Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely ...

principles, it has potential to capture the benefits of both in a single

framework A framework is a generic term commonly referring to an essential supporting structure which other things are built on top of. Framework may refer to: Computing * Application framework, used to implement the structure of an application for an op ...

. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate

nonlinear function In mathematics and science, a nonlinear system is a system in which the change of the output is not proportional to the change of the input. Nonlinear problems are of interest to engineers, biologists, physicists, mathematicians, and many other ...

s. Hence, ANFIS is considered to be a

universal estimator In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished. For example, the ...

. For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by

genetic algorithm In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to gene ...

. It has uses in intelligent situational aware

energy management system An energy management system (EMS) is a system of computer-aided tools used by operators of electric utility grids to monitor, control, and optimize the performance of the generation or transmission system. Also, it can be used in small scale syste ...

ANFIS architecture

It is possible to identify two parts in the network structure, namely premise and consequence parts. In more details, the architecture is composed by five layers. The first layer takes the input values and determines the membership functions belonging to them. It is commonly called fuzzification layer. The membership degrees of each function are computed by using the premise parameter set, namely . The second layer is responsible of generating the firing strengths for the rules. Due to its task, the second layer is denoted as "rule layer". The role of the third layer is to normalize the computed firing strengths, by dividing each value for the total firing strength. The fourth layer takes as input the normalized values and the consequence parameter set . The values returned by this layer are the defuzzificated ones and those values are passed to the last layer to return the final output.

Fuzzification layer

The first layer of an ANFIS network describes the difference to a vanilla neural network. Neural networks in general are operating with a

data pre-processing Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to ...

step, in which the

features Feature may refer to: Computing * Feature (CAD), could be a hole, pocket, or notch * Feature (computer vision), could be an edge, corner or blob * Feature (software design) is an intentional distinguishing characteristic of a software item ...

are converted into normalized values between 0 and 1. An ANFIS neural network doesn't need a

sigmoid function A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula: :S(x) = \frac = \f ...

, but it's doing the preprocessing step by converting numeric values into fuzzy values. Here is an example: Suppose, the network gets as input the distance between two points in the 2d space. The distance is measured in pixels and it can have values from 0 up to 500 pixels. Converting the numerical values into Fuzzy numbers is done with the membership function which consists of semantic descriptions like near, middle and far. Each possible linguistic value is given by an individual

neuron A neuron, neurone, or nerve cell is an electrically excitable cell that communicates with other cells via specialized connections called synapses. The neuron is the main component of nervous tissue in all animals except sponges and placozoa. N ...

. The neuron “near” fires with a value from 0 until 1, if the distance is located within the category "near". While the neuron “middle” fires, if the distance in that category. The input value “distance in pixels” is split into three different neurons for near, middle and far.

References

Fuzzy logic Artificial neural networks {{compu-AI-stub