Applying sliding mode variable structure control to train neural networks is proposed in this paper, which can not only increases learning rate but also improves the stability of neural-network.

A multinational team has cracked a long-standing barrier to reliable quantum computing by inventing an algorithm that lets ordinary computers faithfully mimic a fault-tolerant quantum circuit built on ...

A new algorithm helps topology optimizers skip unnecessary iterations, making optimization and design faster, more stable and ...

Analytical chemistry researchers at the University of Amsterdam's Van 't Hoff Institute for Molecular Sciences (HIMS) have ...

Quantum researchers finally captured the field’s “holy grail,” showing real machines can beat classical computing ...

Prediction of key variables is an important part of the monitoring, control, and optimization of industrial processes, since it is important to anticipate certain behaviors so that the correct actions ...

Pregnant women with type 1 diabetes who used hybrid closed-loop insulin delivery with Control-IQ technology spent more time ...

A research team has achieved the holy grail of quantum computing: an exponential speedup that’s unconditional. By using ...