This paper surveys and discusses the application of data-derived soft-sensing techniques in
biological wastewater treatment plants. Emphasis is given to an extensive overview of the …

The paper proposed to apply a hierarchical fuzzy-neural multi-model and Takagi–Sugeno (T–
S) rules with recurrent neural procedural consequent part for systems identification, states …

Three adaptive neural network control structures to regulate a biological wastewater
treatment process are introduced: indirect, inverse model, and direct adaptive neural control …

Base on the principle of the superposition of waves, active noise control is achieved by
adaptively tuning a secondary source which produces an anti-noise of equal amplitude and …

A direct adaptive neural control scheme with single and double integral‐plus‐state (IPS)
actions is proposed. The control scheme contains two recurrent trainable neural network …

The Recent advances in understanding of the working principles of artificial neural networks
has given a tremendous boost to identification, prediction and control tools of nonlinear …

This paper addresses several pressing concerns in artificial intelligence (ie, explainability,
interpretability, and transparency) that primarily stem from the black-box nature of artificial …

El artículo propone un nuevo sistema de control que contiene un identificador neuronal
recurrente (RNN), un controlador con modos deslizantes (SM) y un término integral. El …

Active noise control accuracy depends on how much destructive interference exists between
the primary noise and the noise (anti noise) generated by secondary source. In this paper …

Abstract A Recurrent Trainable Neural Network (RTNN) with a two layer canonical
architecture and a dynamic Backpropagation learning method are applied for local …