This paper reports the novel results on fractional order-induced bifurcation of a tri-neuron fractional-order neural network (FONN) with delays and instantaneous self-connections by the intersection of implicit function curves to solve the bifurcation critical point. Firstly, it considers the distribution of the root of the characteristic equation in depth. Subsequently, it views fractional order as the bifurcation parameter and establishes the transversal condition and stability interval. The main novelties of this paper are to systematically analyze the order as a bifurcation parameter and concretely establish the order critical value through an implicit function array, which is a novel idea to solve the critical value. The derived results exhibit that once the value of the fractional order is greater than the bifurcation critical value, the stability of the system will be smashed and Hopf bifurcation will emerge. Ultimately, the validity of the developed key fruits is elucidated via two numerical experiments.

The bifurcations in a fractional-order neutral bidirectional associative memory neural network(FONBAMNN) in the inclusion of different neutral delays are deliberated. By the aid of the presented assumptions, the devised FONBAMNN involving four nonidentical delays is subtly transformed into the one with unique delay. Then the outcomes with regard to delay-dependent bifurcations are cultivated by means of the analytic methodology of the characteristic equation. It is evidenced experimentally that the stability performance of the developed FONBAMNN can be neatly maintained when extracting a lesser time delay, and the bifurcation fruits are finely authenticated by employing the bifurcation graphs. Additionally, it proclaims that fractional orders are instrumental in ameliorating the stability of FONBAMNN in comparison with the conventional integer-order counterpart. The efficiency of the developed theory is lastly underpinned by numerical experimentations.