**3.5. Myoelectric signal preprocessing by the neuro fuzzy method**

The step of characterizing the signal is achieved by a neuro fuzzy type ANFIS. The system takes as input the rms values of each pre-processed data acquisition channel. Presents as output the movements characterized that are being carried out by the human arm. The system ANFIS used in this research was implemented using the Matlab tool (Fuzzy Logic Toolbox). The fuzzy neural network is interfaced via Labview, where the routine developed in Matlab is called when needed, being processed in the background.

Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment 355

and the fuzzy rules structure for type Sugeno. This technique was chosen because it obtained good results in preliminary studies cases, its routine is represented in Figure 12. This routine is performed using the MatlabScript node that defines a script to be run in

While creating the initial fuzzy structure was necessary to define the following parameters

• membership function: the Gaussian function was selected because it is a smooth

• radius: was selected the value of 0.1, which represents the influence radius of the cluster, when it is considered a unitary hypercube. The smaller the radius, more clusters

In the first Subject assay, the expected input and output values are used to create the system initial structure representing the fuzzy network of 8 inputs, 60 clusters (i.e., 60 rules) e one output, generated for a system assay, and adjust it later to adapt to represent more faithfully

After creating the initial fuzzy structure is necessary to adapt the membership functions for the data acquired in the session, thus making a fine adjustment of the functions, leading to results more consistent with the ones expected. The adaptation step is very important, because it help to better define the limits and parameters of the membership functions, leaving the model best suited for the Subject. In this step were used a hybrid training function. The hybrid training is a combination of the gradient method with the LSE method to optimize the time convergence of the model, since it reduces the demand on the dimensional space. This function used the following parameters, according to the routine

**Figure 12.** Routine developed for definition of the fuzzy network structure.

• output data: array with the expected output for the input data set;

function on the edges, have shown better results in these trials;

are created and, consequentially, a greater number of rules.

which utilized the MatlabScript function represented in Figure 13:

• initial fuzzy network, created from the subtractive clustering technique;

• output data: vector with the expected output for the input data set;

• input data: array with fuzzy inputs of the network;

a model that can characterize the Subject movements.

• input data: array with the network fuzzy inputs;

Matlab.

in Matlab:
