Global Asymptotical Stability of Neutral-Type Neural Networks with <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.4619007pt" id="M1" height="11.8613pt" version="1.1" viewBox="-0.0657574 -11.3994 23.4361 11.8613" width="23.4361pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-69" d="M743 375C743 570 607 650 407 650H136L130 622C214 612 220 606 204 526L125 122C110 43 105 36 24 27L17 0H250C382 0 482 23 571 76C680 141 743 248 743 375ZM644 379C644 188 520 36 305 36C277 36 249 38 229 46C205 55 202 71 212 126L293 553C300 588 306 600 317 606C330 613 356 617 390 617C547 617 644 537 644 379Z"/></g><g transform="matrix(.017,0,0,-0.017,13.145,0)"><path id="g117-33" d="M535 230V280H52V230H535Z"/></g></svg>Operator and Mixed Delays

<div>State trajectories of system (<a href="https://static-preview.hindawi.com/articles/mpe/volume-2020/3096762/figures/#EEq47">47</a>) with <span class="nowrap"><svg height="9.16851pt" id="M178" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.96212 35.8618 9.16851" width="35.8618pt" 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="M620 675H597C578 656 570 650 541 650H144C112 650 104 653 94 675H72C59 618 42 552 23 493L53 491C71 534 88 564 105 585C124 608 144 615 238 615H290L197 121C182 40 174 34 88 28L82 0H361L367 28C275 34 266 38 281 121L374 615H441C522 615 543 608 553 583C562 560 566 531 565 493L597 494C603 551 612 629 620 675Z"></path></g><g transform="matrix(.013,0,0,-0.013,11.917,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,23.18,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,29.42,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g></svg>.</span></div>

Mathematical Problems in Engineering

fig1

Figure 1

Figure 1: Global Asymptotical Stability of Neutral-Type Neural Networks with <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.4619007pt" id="M1" height="11.8613pt" version="1.1" viewBox="-0.0657574 -11.3994 23.4361 11.8613" width="23.4361pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-69" d="M743 375C743 570 607 650 407 650H136L130 622C214 612 220 606 204 526L125 122C110 43 105 36 24 27L17 0H250C382 0 482 23 571 76C680 141 743 248 743 375ZM644 379C644 188 520 36 305 36C277 36 249 38 229 46C205 55 202 71 212 126L293 553C300 588 306 600 317 606C330 613 356 617 390 617C547 617 644 537 644 379Z"/></g><g transform="matrix(.017,0,0,-0.017,13.145,0)"><path id="g117-33" d="M535 230V280H52V230H535Z"/></g></svg>Operator and Mixed Delays 

Mathematical Problems in Engineering

Global Asymptotical Stability of Neutral-Type Neural Networks with Operator and Mixed Delays

Figure 1