# update

Update fuzzy rule using fuzzy inference system

## Syntax

``ruleOut = update(ruleIn,fis)``

## Description

example

````ruleOut = update(ruleIn,fis)` updates the fuzzy rule `ruleIn` using the information in fuzzy inference system `fis` and returns the resulting fuzzy rule in `ruleOut`.```

## Examples

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Create a fuzzy rule using a verbose text description.

`rule = fisrule("if service is poor and food is delicious then tip is average (1)");`

Alternatively, you can specify the same rule using a symbolic text description.

`rule = fisrule("service==poor & food==delicious => tip=average")`
```rule = fisrule with properties: Description: "service==poor & food==delicious => tip=average (1)" Antecedent: [] Consequent: [] Weight: 1 Connection: 1 ```

Before using `rule` with a fuzzy system, update the rule `Antecedent` and` Consequent` properties using the `update` function.

```fis = readfis("tipper"); rule = update(rule,fis)```
```rule = fisrule with properties: Description: "service==poor & food==delicious => tip=average (1)" Antecedent: [1 2] Consequent: 2 Weight: 1 Connection: 1 ```

Create a fuzzy rule using a numeric description. Specify that the rule has two input variables.

`rule = fisrule([1 2 2 0.5 1],2)`
```rule = fisrule with properties: Description: "input1==mf1 & input2==mf2 => output1=mf2 (0.5)" Antecedent: [1 2] Consequent: 2 Weight: 0.5000 Connection: 1 ```

Before using `rule` with a fuzzy system, update the rule `Description` property using the `update` function.

```fis = readfis("tipper"); rule = update(rule,fis)```
```rule = fisrule with properties: Description: "service==poor & food==delicious => tip=average (0.5)" Antecedent: [1 2] Consequent: 2 Weight: 0.5000 Connection: 1 ```

## Input Arguments

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Fuzzy rule, specified as a `fisrule` object or an array of `fisrule` objects. If `ruleIn` was created using a:

• Text description, its `Antecedent` and `Consequent` properties are updated using the input and output membership function indices in `fis` that correspond to the membership function names in the `Description` property of `ruleIn`

• Numeric description, its `Description` property is updated using the input and output membership function names in `fis` that correspond to the membership function indices in the `Antecedent` and `Consequent` properties of `ruleIn`

If you specify `ruleIn` as an array of `fisrule` objects, then all of the rules are updated accordingly.

Fuzzy inference system, specified as one of the following:

## Output Arguments

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Fuzzy rule, returned as a `fisrule` object or an array of `fisrule` objects.

## Version History

Introduced in R2018b