Fuzzification of neural networks show great promise in improving system reliability and
computational efficiency. In the present work we explore the possibility of combining fuzzy …

The neuro-fuzzy system is network which resemble human-like operation of the naturally
imprecise data and decision-making. This paper proposes implementation of the …

Neural networks implemented with traditional hardware face inherent limitation of memory
latency. Specifically, the processing units like GPUs, FPGAs, and customized ASICs, must …

Realizing adaptive brain functions subserving perception, cognition, and motor behavior on
biological temporal and spatial scales remains out of reach for even the fastest computers …

The definition of a memristor was introduced by Professor LO Chua in 1971. By generalizing
the definition of a memristor, memristive devices and systems were also proposed. Since the …

Fuzzy techniques can be used for accurate and high-speed modeling as well as for the
control of complex systems, but various challenging problems are usually encountered …

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

The hardware implementation of neuromorphic system requires energy and area-efficient
hardware. Memristor-based hardware architectures is a promising approach that naturally …

Memristors have recently attracted significant interest due to their applicability as promising
building blocks of neuromorphic computing and electronic systems. The dynamic …

Scientists have dreamed of an information system with cognitive human-like skills for years.
However, constrained by the device characteristics and rapidly increasing design complexity …