Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping.

Solving Robotic Trajectory Sequential Writing Problem via Learning Character's Structural and Sequential Information.

The writing sequence of numerals or letters often affects aesthetic aspects of the writing outcomes. As such, it remains a challenge for robotic calligraphy systems to perform, mimicking human writers' implicit intention. This article presents a new robot calligraphy system that is able to learn writing sequences with limited sequential information, producing writing results compatible to human writers with good diversity.

Encryption and Decryption of Audio Signal and Image Secure Communications Using Chaotic System Synchronization Control by TSK Fuzzy Brain Emotional Learning Controllers.

In this article, a new idea of chaos synchronization and chaos-based secure communication is developed. First, the chaotic master system is used as a transmitter in chaos-based secure communication, then a drive signal is constructed, and the information message is encrypted into the drive signal to form a transmitted signal for secure communication. Second, in the receiver, a recurrent Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning cerebellar model articulation controller (RTFBECAC) is developed to control the slave system to follow the master system in the transmitter.

Decoder Choice Network for Metalearning.

Metalearning has been widely applied for implementing few-shot learning and fast model adaptation. Particularly, existing metalearning methods have been exploited to learn the control mechanism for gradient descent processes, in an effort to facilitate gradient-based learning in gaining high speed and generalization ability. This article presents a novel method that controls the gradient descent process of the model parameters in a neural network, by limiting the model parameters within a low-dimensional latent space.

Global feather orientations changed by electric current.

During chicken skin development, each feather bud exhibits its own polarity, but a population of buds organizes with a collective global orientation. We used embryonic dorsal skin, with buds aligned parallel to the rostral-caudal body axis, to explore whether exogenous electric fields affect feather polarity. Interestingly, brief exogenous current exposure prior to visible bud formation later altered bud orientations.

Hybrid Neural Network Cerebellar Model Articulation Controller Design for Non-linear Dynamic Time-Varying Plants.

This study proposes a hybrid method to control dynamic time-varying plants that comprises a neural network controller and a cerebellar model articulation controller (CMAC). The neural-network controller reduces the range and quantity of the input. The cerebellar-model articulation controller is the main controller and is used to compute the final control output.

Ultra-broadband near-infrared emission CuInS/ZnS quantum dots with high power efficiency and stability for the theranostic applications of mini light-emitting diodes.

Broadband near-infrared CuInS/ZnS quantum dots with up to 94.8% quantum yield were synthesized with fast precursor decomposition leading to monomer conversion improvement. In the mini-LED package, the device showed high power efficiency and stability was also demonstrated with a penetration test and vein imaging showing its potential biomedical application in the theranostics field.

Type-2 Fuzzy Hybrid Controller Network for Robotic Systems.

Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller.

An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots.

The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots.

Wavelet Fuzzy Brain Emotional Learning Control System Design for MIMO Uncertain Nonlinear Systems.

This paper aims to present a novel efficient scheme in order to more effectively control the multiple input and multiple output (MIMO) uncertain nonlinear systems. A wavelet fuzzy brain emotional learning controller (WFBELC) model is proposed, which is comprises the benefit of wavelet function, fuzzy theory and brain emotional neural network. When it is used as the main tracking controller for a MIMO uncertain nonlinear systems, the performances of the system, such as the approximation ability, the learning performance and the convergence rate, will be effectively improved.

Broadband Cr , Sn -Doped Oxide Nanophosphors for Infrared Mini Light-Emitting Diodes.

Light-emitting diodes break barriers of size and performance for displays. With devices becoming smaller, the materials also need to get smaller. Chromium(III)-doped oxide phosphors, which emit near-infrared (NIR) light, have recently been used in small electronic devices.

A bioengineered retinal pigment epithelial monolayer for advanced, dry age-related macular degeneration.

Retinal pigment epithelium (RPE) dysfunction and loss are a hallmark of non-neovascular age-related macular degeneration (NNAMD). Without the RPE, a majority of overlying photoreceptors ultimately degenerate, leading to severe, progressive vision loss. Clinical and histological studies suggest that RPE replacement strategies may delay disease progression or restore vision.

A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification.

The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN).

Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.

This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC.

Volume polarization holographic recording in thick photopolymer for optical memory.

Based on a vector wave theory of volume holograms, dependence of holographic reconstruction on the polarization states of the writing and reading beams is discussed. It is found that under paraxial approximation the circular polarization holograms provide a better distinction of the reading beams. Characteristics of recording polarization holograms in thick phenanthrenequinone-doped poly(methyl methacrylate) (PQ/PMMA) photopolymer are experimentally investigated.

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization.

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC).

Field induced dynamic waveguides based on potassium tantalate niobate crystals.

In this paper, a new type of optical waveguide based on potassium tantalate niobate (KTN) electro-optic crystal is presented. The guiding property of the optical waveguide can be quickly (on the order of nanosecond) tuned and controlled by the applied external electric field, which can be useful for many applications such as broadband ultrafast optical modulators, variable optical attenuators, and dynamic gain equalizers.

Adaptive control for mimo uncertain nonlinear systems using recurrent wavelet neural network.

Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, good generalization capability and information storing ability. With these advantages, this paper proposes an RWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The RBAC system is composed of a neural controller and a bounding compensator.

Adaptive filter design using recurrent cerebellar model articulation controller.

A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RCMAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted.