Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling

<div>Outputs of the proposed FWA-LSSVR approach with <svg height="8.8423pt" id="M225" style="vertical-align:-0.2064009pt" version="1.1" viewbox="-0.0498162 -8.6359 8.28119 8.8423" width="8.28119pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 608S337 618 358 618C432 618 480 575 480 485Z"></path></g></svg>=4, 6, 8, and 10 SRMs are shown, respectively.</div>

Mathematical Problems in Engineering

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Figure 4

Figure 4: Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling 