An Efficient Second-Order Algorithm for Self-Organizing Fuzzy Neural Networks

Multiobjective Integrated Optimal Control for Nonlinear Systems

The multiobjective optimal control method optimizes the performance indexes of nonlinear systems to obtain setpoints, and designs a controller to track the setpoints. However, the stepwise optimal control method that independently analyzes the optimization process may obtain unfeasible and difficult to track setpoints, which will reduce the operation and control performance of the systems. To solve this problem, a multiobjective integrated optimal control (MIOC) strategy is proposed for nonlinear systems in this article. The main contributions of MIOC are threefold. First, in the framework of multiobjective model predictive control, an integrated control structure with a comprehensive cost function and a collaborative optimization algorithm is designed to achieve the coordinate optimal control. Second, for the time inconformity of setpoints and control laws caused by the characteristic of tracking control, the different prediction horizons are designed for the comprehensive cost function. Then, the collaborative optimization algorithm is proposed for the comprehensive cost function to achieve the integrated solution of setpoints and control laws to enhance the operation and control performance of nonlinear systems. Third, the stability and control performance analysis of MIOC is provided. Finally, the proposed MIOC method is applied for a nonlinear system to demonstrate its effectiveness.

Novel Extended State Observer Design for Uncertain Nonlinear Systems via Refined Dynamic Event-Triggered Communication Protocol

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

Type-2 Fuzzy Broad Learning System

The broad learning system (BLS) has been identified as an important research topic in machine learning. However, the typical BLS suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a type-2 fuzzy BLS (FBLS) is designed and analyzed in this article. First, a group of interval type-2 fuzzy neurons was used to replace the feature neurons of BLS. Then, the representation of BLS can be improved to obtain good robustness. Second, a fuzzy pseudoinverse learning algorithm was designed to adjust the parameter of type-2 FBLS. Then, the proposed type-2 FBLS was able to maintain the fast computational nature of BLS. Third, a theoretical analysis on the convergence of type-2 FBLS was given to show the computational efficiency. Finally, some benchmark and practical problems were used to test the merits of type-2 FBLS. The experimental results indicated that the proposed type-2 FBLS can achieve outstanding performance.

Fuzzy Adaptive Switching Control for Stochastic Systems With Finite-Time Prescribed Performance

The issue of fuzzy adaptive switching control for stochastic systems with arbitrary switching signal and finite-time prescribed performance is investigated in this article. A piecewise function is adopted to characterize finite-time prescribed performance, and the error signal is converted to a new state variable via the tangent function. Unknown functions are approximated via fuzzy-logic systems (FLSs). Based on the stochastic stability theory and common Lyapunov function, a fuzzy adaptive switching control scheme is presented. The control law is proposed for the stochastic switched closed-loop system so that not only all the signals are ensured to be semiglobally uniformly ultimately bounded (SGUUB) in probability but also a residual error related to the finite-time prescribed performance bound is guaranteed. Eventually, simulation studies for a practical system are given to show the effectiveness of the presented fuzzy adaptive switching control scheme.

BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis

Sentiment analysis uses a series of automated cognitive methods to determine the author's or speaker's attitudes toward an expressed object or text's overall emotional tendencies. In recent years, the growing scale of opinionated text from social networks has brought significant challenges to humans' sentimental tendency mining. The pretrained language model designed to learn contextual representation achieves better performance than traditional learning word vectors. However, the existing two basic approaches for applying pretrained language models to downstream tasks, feature-based and fine-tuning methods, are usually considered separately. What is more, different sentiment analysis tasks cannot be handled by the single task-specific contextual representation. In light of these pros and cons, we strive to propose a broad multitask transformer network (BMT-Net) to address these problems. BMT-Net takes advantage of both feature-based and fine-tuning methods. It was designed to explore the high-level information of robust and contextual representation. Primarily, our proposed structure can make the learned representations universal across tasks via multitask transformers. In addition, BMT-Net can roundly learn the robust contextual representation utilized by the broad learning system due to its powerful capacity to search for suitable features in deep and broad ways. The experiments were conducted on two popular datasets of binary Stanford Sentiment Treebank (SST-2) and SemEval Sentiment Analysis in Twitter (Twitter). Compared with other state-of-the-art methods, the improved representation with both deep and broad ways is shown to achieve a better F1 -score of 0.778 in Twitter and accuracy of 94.0% in the SST-2 dataset, respectively. These experimental results demonstrate the abilities of recognition in sentiment analysis and highlight the significance of previously overlooked design decisions about searching contextual features in deep and broad spaces.

A Novel Self-Organizing Fuzzy Neural Network to Learn and Mimic Habitual Sequential Tasks

In this article, a new self-organizing fuzzy neural network (FNN) model is presented which is able to simultaneously and accurately learn and reproduce different sequences. Multiple sequence learning is important in performing habitual and skillful tasks, such as writing, signing signatures, and playing piano. Generally, it is indispensable for pattern generation applications. Since multiple sequences have similar parts, local information such as some previous samples is not sufficient to efficiently reproduce them. Instead, it is necessary to consider global and discriminative information, maybe in the very initial samples of each sequence, to first recognize them, and then predict their next sample based on the current local information. Therefore, the structure of the proposed network consists of two parts: 1) sequence identifier, which computes a novel sequence identity value based on initial samples of a sequence, and detects the sequence identity based on proper fuzzy rules and 2) sequence locator, which locates the input sample in the sequence. Therefore, by integrating outputs of these two parts in fuzzy rules, the network is able to produce the proper output based on the current state of each sequence. To learn the proposed structure, a gradual learning procedure is proposed. First, learning is performed by adding new fuzzy rules, based on coverage measure, using available correct data. Next, the initialized parameters are fine-tuned, by the gradient descent algorithm, based on fed back approximated network output as the next input. The proposed method has a dynamic structure able to learn new sequences online. Finally, to investigate the effectiveness of the presented approach, it is used to simultaneously learn and reproduce multiple sequences in different applications, including sequences with similar parts, different patterns, and writing different letters. The performance of the proposed method is evaluated and compared with other existing methods, including the adaptive network-based fuzzy inference system, GDFNN, CFNN, and long short-term memory (LSTM). According to these experiments, the proposed method outperforms traditional FNNs and LSTM in learning multiple sequences.

Synchronization of Coupled Harmonic Oscillators With Asynchronous Intermittent Communication

This paper adopts two different approaches, the small-gain technique and the integral quadratic constraints (IQCs), to investigate the synchronization problem of coupled harmonic oscillators (CHOs) via an event-triggered control strategy in a directed graph. First, a novel control protocol is proposed such that every state signal of the CHO decides when to exchange information with its neighbors asynchronously. Then, the resulting closed-loop system based on the designed control protocol is converted into a feedback interconnection of a linear system and a bounded operator, and the stable condition of the feedback interconnection is presented by employing the small-gain technique. In order to better describe the relationship between the input and output, the IQCs theorem is applied to derive the stable condition on the basis of the Kalman-Yakubovich-Popov lemma. Finally, a simulation example is provided to verify the proposed new algorithms.